{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Network Tour of Data Science, EPFL 2016\n",
    "# Project: Facial Emotion Recognition\n",
    "Dataset taken from: kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge\n",
    "<br>\n",
    "<br> Ref: \"Challenges in Representation Learning: A report on three machine learning\n",
    "contests.\" I Goodfellow, et al.\n",
    "<br>\n",
    "<br>\n",
    "students: Patryk Oleniuk, Carmen Galotta\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project presented here is an algorithm to recognize and detect emotions from a face picture. \n",
    "\n",
    "Of course, the task of recognize face emotions is very easy for humans to do even if somethimes is really hard to understand how a person feels, but what can be easily understood thanks to human brain, is difficult to emulate by a machine.\n",
    "\n",
    "The aim of this project is to classify faces in discrete human emotions. Due to the success of Convolutional Neural Network in images classification tasks it has been tought that employing it could be a good idea in face emotion as well.\n",
    "\n",
    "The dataset has been taken from the kaggle competition and consists of 35k grey images with size 48x48 pixels already labeled with a number coding for classes of emotions, namely: \n",
    "\n",
    "0-Angry<br>\n",
    "1-Disgust<br>\n",
    "2-Fear<br>\n",
    "3-Happy<br>\n",
    "4-Sad<br>\n",
    "5-Surprise<br>\n",
    "6-Neutral<br>\n",
    "\n",
    "The faces are mostly centered in the image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configuration, dataset file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data in the dataset: 1200\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import scipy.misc\n",
    "import time\n",
    "import collections\n",
    "import os\n",
    "import utils as ut\n",
    "import importlib\n",
    "import copy\n",
    "\n",
    "importlib.reload(ut)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (20.0, 20.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# The CSV data (with removed first line ! (names))\n",
    "emotions_dataset_dir = 'fer2013_shortened.csv'\n",
    "\n",
    "#obtaining the number of line of the csv file\n",
    "file = open(emotions_dataset_dir)\n",
    "numline = len(file.readlines())\n",
    "print ('Number of data in the dataset:',numline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data from *.csv file \n",
    "The first step is to load the data from the .csv file. <br> The  format of the csv line is<br>\n",
    "class{0,1,2,3,4,5,6},pix0 pix2304,DataUsage(not used)<br>\n",
    "e.g.<br>\n",
    "2,234 1 34 23 ..... 234 256 0,Training<br>\n",
    "The picture is always 48x48 pixels, 0-255 greyscale.\n",
    "\n",
    "# Data cleaning:\n",
    "## 1.Remove strange data\n",
    "In the database there are some images thar are not good (e.g. some images are pixelated, unrelevant, from animations).\n",
    "It has been tried to filter them by looking at the maximum of the histogram. If the image is very homogenous, the maximum value of the histogram will be very high (that is to say above a certain threshold) then this image is filtered out. Of course in this way are also removed some relevant information, but it's better for the CNN not to consider these images.\n",
    "## 2.Merge class 0 and 1\n",
    "We discovered that class 1 has a very small amount of occurance in the test data et. This class, (disgust) is very similar to anger and that is why we merger class 0 and 1 together.\n",
    "Therefore, the recognized emotions and labels are reduced to 6:<br>\n",
    "0-(Angry + Disgust)<br>\n",
    "1-Fear<br>\n",
    "2-Happy<br>\n",
    "3-Sad<br>\n",
    "4-Surprise<br>\n",
    "5-Neutral<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Images. It may take a while, depending on the database size.\n",
      "loaded 43% of dataset (525/1175). Filtered images: 25\n",
      "loaded 87% of dataset (1053/1147). Filtered images: 53\n",
      "Skipped 94 happy class images.\n",
      "1053 are left after 'strange images' removal.\n",
      "Deleted 53 strange images. Images are shown below\n"
     ]
    }
   ],
   "source": [
    "#Load the file in csv\n",
    "ifile  = open(emotions_dataset_dir, \"rt\")\n",
    "reader = csv.reader(ifile)\n",
    "\n",
    "hist_threshold = 350 # images above this threshold will be removed\n",
    "hist_div = 100 #parameter of the histogram\n",
    "\n",
    "print('Loading Images. It may take a while, depending on the database size.')\n",
    "images, emotions, strange_im, num_strange, num_skipped = ut.load_dataset(reader, numline, hist_div, hist_threshold)\n",
    "\n",
    "ifile.close()\n",
    "\n",
    "print('Skipped', num_skipped, 'happy class images.')\n",
    "print(str( len(images) ) + ' are left after \\'strange images\\' removal.')\n",
    "print('Deleted ' + str( num_strange ) + ' strange images. Images are shown below')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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17Tve8Q7NoCqaV/dGR0cJh8P09fVpGk4mk+zbt48dO3boQ61iQktLS3G73fT1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\nBEEQBGHB8P8AXzG0h6V1LcQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131d7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing strange images\n",
    "plt.rcParams['figure.figsize'] = (5.0, 5.0) # set default size of plots\n",
    "idxs = np.random.choice(range(1,num_strange ), 6, replace=False)\n",
    "for i, idx in enumerate(idxs):\n",
    "    plt_idx = i\n",
    "    plt.subplot(1, 6, plt_idx+1)\n",
    "    plt.imshow(strange_im[idx])\n",
    "    plt.axis('off')\n",
    "    if(i == 0):\n",
    "        plt.title('Some of the images removed from dataset (max(histogram) thresholded)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the correct data\n",
    "Here random pictures from each class are shown. As we can see some of them are with glasses or covered with long hair as well as some pictures taken from sideways. This is, of course, an additional challenge when training the neural network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r6VCNYVLmIUkxyyBEp5twREd+N52jjDa4+g11XmZKQ7uo09XBY86/MWylqwUz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\n63l+K86ZlpimEcC64EWwMmWeWwnQrPGV61pY5o6Gt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QJLDHuEvwdzNhD072cS1zV55zfW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rZBsZkO+aWfZG9kEPnuXZyKnvg0nB7gk/07rnfaCdjJVbE5EPNHo+nvQZ65zThP7i\nL/6ijgVkr2n5GwXshahnGz0tAeLeF/aHlhSOcc0Ei5L0ohe9SNI21rfccsuuDJlIan0P3k3riWvm\n+btpNbHO0G63MiCvjdo3rTSNxpcy1o63FxlmnXh70yJw2mC+Xv/61+9+gyqeOfVxYGzYm9tYgRXp\nQdu3c226Fpsx4jcPeMeayzXtTAfsG75WkXm07uz/0pa0m7Xu1lf2zEa2kzTPjVAJ+cCK7cQTWLrZ\n+0ieK2303Mew6NNel4fnP//5kjaLzPd93/ftyrCeNu8Hxppzzt9LU258rXCGsFb8nEpqdL+P/YJ1\n7PfhScE57N49rEXkHSIgaRt/PGrcypPWE38vzT3Pzw2ex78tqXNDUnm3/ekkGIvMYDAYDAaDwWAw\nOHc4ExYZknu5D+NLX/pSSVsSJdca8LWHdQFqU2nzfW8xMknH6V/aXI8W1bVGfCGjkWx0gy0JZcK/\nMPnybMkyk0rWn5f1u48mbeFf17D/8i//sqQtvsE1H1gH3vCGN0ja107whc3XfvOxPgZaMknGAy2T\na0pTy+f3MQ7MsWsuGq1tljXf56TVdM0V80z7mvUErYZr5lM+fd7R9tDPRg+O1tPnndiMtCpKh768\nPrf8TV8+/OEPH7SdMk/Od8wYmaQb9mc3rRR/o7lqvuCU4VcvST/7sz8r6dAyI21yguXB1zQaXbR1\n3k7mD81coxVPKmm/DouK15lac7cOtMSLIJP+utYUWVoldaNOlzPGo+2tJGw8JpoWvLUl+9DuZ67o\nn2szKUMOXPOIvDHHLhvM38033yxpf/5JLIxMJnWu152eA9K2BlpcQ1oLfI2vLPhpgWkaeuSdut2q\nSSwA+0/zKjgW0Jo/4xnP2P3G+ibGzc+LpD9u458WXx/HjLNqGugW1wkN+vvf/35J0mc+85ldGeOX\nSShbwm3+9X0DQEHtqRKQ3Ze//OWS9sdiFSeV1rl2LXLOHPD+Jm37E3uun6esgWPE3bb3lEzSS9yf\nJL3whS+UtMXNtNjRRx55RNJ+HBzj2KwZUE5zf6OlZh926yWy1bwmWFMtVpw1lvGQ0vZewlnSziLu\n9/OGv2m7W2Q48zijm5yCVRyMr52LSa49FpnBYDAYDAaDwWBw7jAfMoPBYDAYDAaDweDc4Uy4lv25\nP/fnJO3T8WE2w8WhuedgLobaTtrM7G6eA5lJ1wOnMmjUA7TTRcWzo2Nexwztri1phvaydClzM11S\ne7opHvMeZsTMXu7Pffe73737DdMy5jrv31/+y39ZkvSmN71pr25vX8tMfDGZVy8WBKxjkpU2M25z\nk0oCh+Y2yLi62ZPn4Bbh/Uu6yRbguaIlRV7cNYn7CIb0wHz62ihCMwDY548y5At3DmmjmUyzvrS5\na3KfjwsmbEzTd911164M0zRr093JCO49BpB9DyqmX4ynm9DpQ+sf+wOy4VnYcW8gG7u7ASDzTpoA\n0h3H5492Mna+F7CH8JtTnCcVrLu7pAupr0fGKl0FpM2dERp55E3a5Io6/T7KCJx2uSZoljHAXTXb\nddpobiPp4tPcFRq1KkBOGBd3vUEWGAt3O0MucbNz2nzOM8aVgGtpcylZnU+ZTd3dCJuLSCKDeP23\nRvGdbotc66QR7IusfwK2pc1d6l/9q38laf9sPyZtv7SNh78D4Db9rne9S9L+fgYFO3OLbLQ0Bi2Q\nm7Fp+0yuP98feW9hn3G6dmSHseUZvv45O3Ap8zL2ba7BXUraCE6SJMKR8y5dWAb97PvkJz8pSbrp\nppv2ni8d0ub7WdTG+rTQSHByrbhrOcQcTskMmHfeu/wcYG/nDHIZYd/xd0fAGOOe5WdYkjv5ucH+\nlPT90ub6xj715Cc/+aAPjLnPH2ua8fD3DOrnXz8Xue5i3UaTIKO9Y50EY5EZDAaDwWAwGAwG5w5n\nwiLz/d///ZL2vwzRkPPl7IFsSUGJtkXaNEF8Nbp1gS9mvjZdq5UWHP9i53p+cw0dX41oK7wsg8z9\nCzMDdFtwOuPhX8X8zdewkyCgkfkX/+JfSNoPHuQr/Prrr5ck/bW/9td2ZRAruLYNuGbV2+3wQLnT\nAtq9lnCuIWmJXcOTNH6u0ad+tENelkGJTTsFmmaYuv0+tEJN28D4p7z535lETdrkBQ27a9+Qa+TG\n5TP7521Cg4xWyjVWSTXe2nIMULdrutFwsd49OHEVfJsUor6OSM7GmENh6b+1hITUyZg3Ugloz32v\nS6paH+u0FPuelUQTrpHzNvv90kaM0EgemPeWnI3rsMi0uabvLRHxMdDWUWr3VqQkq7JG845VGGuU\nB/vefffdkjYrjWucn/CEJ0jaZNdlI4OECQJ3MpMM5G9WJmTf9yb+Tspt6XCOfZ/Kc4359HOY/jFO\n3t5v//Zvl7TRtvuaPeYeIXWLClZqyBb+5t/8m7sytOpYr7GIumzkXuJlyHzS9UubDHKfjx+pDbDO\neQLPtM5lsk5pW+PMYyN5IWmtl/HuQFtcFjOJsMsL+xL7DH2DSMjr+oN/8A/u1SMd0n279r7tp6eF\nZiFhrSS9tbRPEiH1dx767gQXUOS3BNP5zupJpJlH/vUzjLWVCWa9XY1+m+dBle99oP5mUUtLjO8R\n/Mb6cG8g/m407el91BJiIhttzE6CscgMBoPBYDAYDAaDc4czYZFJLYC0fcXxm2u3kv7vO7/zO3dl\naMjwzXVfWJ6DT6xrIrA88OXrZS1WBaDJbfEXaPe5xjWt+cXbfLVbgjy+utGqusXk7/ydv7PXB6c+\nhFIZTZn3JeNn/Es4taletrKQPFYw5o06NWmKvaxRw3J9s3RwH/Ep9957766M6xqdYtbt45lUpy3J\nJnLq1jbkpSUazaServ3EIoYfupcRO5LWAv8b7YlTpBJHgbbNtSi5Dla++aeJldYGf2HfJyhjXP2+\npNF1TRfJ49CUvu997zuoc2WxoJ1+DVosxs7n3X2NvR5p056h8fIy9hXmxi0yGdflPtLMO21Bk+i/\nYR30uU5N6sc+9rFdGXvOjTfeKGmf0vliEptdLBqdcqMhvhi0uCLAc+iTW/LQgjPWrmEn2SHr3tca\ndbHmkJsWp7mKZ2iJe6mrWafSP92178gP+z97iu+rSZHv481+w7pEMywdxkqcNljbLYbyec97nqT9\nmJ2Pf/zjkrZ5QHZX8X7NWsP9Lu9p8XMLHuOQMTb+bOQbWWzWfebPk14yBsiU9yVj+JoFj3/dQyEt\nVaS9cGrnN7/5zXvPbwljQYv3Owawhvo+m1YJ90bh/ZB/kQ/pMDbW54N3TdZ/i4nmfu87ewhj3s4W\nnuPve+wPma5D2mKrWnoIwHNaUnX+9XdPZIGz0mWqpa/IOsEq0ayjWW4uhLHIDAaDwWAwGAwGg3OH\n+ZAZDAaDwWAwGAwG5w5nwrUMuCk4A/bcPScDGN1UhekYM5u7P/z8z/+8pC1zt9NAYkYEToGK2dHd\nAQDmNtrZ3I8wB7Zsp5j3vCzduTxzOoGht912m6T9jOsEMn73d3/3QZ8YI+pyFwHcqlrW4jSbuwnw\nYkx/Xy/aM5JC1f+mne5ClYGL7rKBSRQzPWZoSXr44Ycl9eA9cBIqaDcjZx1OXUw7MbN7YD7mX35z\n+lbkmHY6jSey1+jBcxzdJe2nfuqnJG3Buh6ciEsEstuCE4+BVUBoo9Nlblb3tWzYmM4hT2AMpC3j\ndnNz4zlQX+LWIR3SU7rLRlLdNpKHFuwLqNPdnJwaOesEuP34WsF1NV2KvJ24FLhrwe233y5pIxgh\nyFjaMohfccUVB214rMhgav+tUXoin+nS6+Oa17jbC3UxHy7vzDFj9h3f8R27sg9+8IOSNllyNxfc\nP6mb/dhdTHhOIxBhn2EdNnfAllYgzx7fpzjr+Jf++nNpS1L0ZxuyLy77xwDt8HOUfrNH/sk/+Sd3\nZaxXXAEZK6dkx7Wz1X0SuuZ0SZS2dXffffdJ2l9P7PeMMa7k7gLL3sWcuJwz/kmZK23ubS2tAHPJ\nuegB3cgZ71TvfOc7JUk//uM/vruGdiLfzc2uuckdk6IdYiOnSqfvyKW7RjE+f+JP/AlJ+65lSVnu\n7yCMMXTa0FxLh/uvj0sSOvh+TDvTzV3a3uHYxxsxUnOL5zfu9z4gX6xR36d4J0ZOXabynby9B/Mc\nH4s8m72sudhfCGORGQwGg8FgMBgMBucOZ8Ii0ygN+a1pw/mabV/21EHgm2sGoVUlgBmtsyT9o3/0\njyRtiZygKZa2r1u+6F0rkppPR36Btv41TQRfuliCWtDY85//fEnSj/3Yj+3K6DPt83by5XvJJZdI\n6pqSlsivBadlW46BpmlNjVej+GOsfMwyKLUFsAEPfM7kk402FK1Bsya2gECuQ+viwZ9OUSr15JXI\nmyfWog1YYtwiQ9+53608lLFGPDj5rW99q6QteNutLklf7sGix7TIIAvtGW0d0WfKWtLZZlnJRJpO\nZ/22t71N0ka76fc1amyA7DZCB+SD9rmVDkth7nleB+Pv8pmJc1vyOX7zcUHWm2aNv7nf25KU3Gi5\nJemnf/qnJUmvf/3rddpoZwdoWtNMcste63tLJjZ063xqGl17ynOYY38u1nEsvmjhpW09ESTMGl1R\nQzuSmt33NPqHddA1nmhykbdGzZ5wDWoGEjeLBPLge+7qzDwNtHMsA6n9veCHfuiHJEk/8RM/IWnT\nwLt88z7A2HrQdVr+msaZ8WxeGez7TpnO2Z2eAh7Qj3cFlO5+3iPn/Ob3Mf7NSptEF14npCdYYn7w\nB39Q0pZAUtrkLSmp/e+WXNvP69MGfXDCE35jH3fZZXxe8YpXSNpP9AqxQSPW4DfWthNcYN1vKQFW\nJEbIT/M0oawlS85kl77+2CeaFwPrIwP7pW1PR/YvNjl6e6dbneljkRkMBoPBYDAYDAa/pnEmLDIt\nkRR/89XpX8yZyMe/KFOT5JpkNE7f8z3fI0l6zWtesyt7xzveIUm69dZbJe1TrqLNQHvuVH1ov1OL\n6781v3baTPvc35C+EgeB9UXaknbhH+tJkPhipsy19nytN21Vfg23eQDHtMI4+FL3ec/Eos1yxBy3\n5KNc7zKBNrppb5n3+++/f68eR9KEex1J1Sgd0lq63y6y0zSxzCky5fFambzO788EfK51xZcfGXLr\nHnXyPJfPFd3yMamYmf9mieM3t0owt6s2NTpk5iZ9kKWNxhzrlcevUQc0nM2/PGNlpG1uPcYFMDc5\nx9KmZWNfci0x16Nd9HlPy0qLJSDG0BO+MY6sC9cAcn2zYhybblda0xI3rTBnCD7gPh/0Be2njzlj\nxf3et7QO+XNpC/PhVgmsJWjkfewA2slMSidtWtOklHUgy34mIG+rfYc55lxdJf71OaC/jCGxq9K+\nxfEYyLNO2samJWMkTcGP/MiPSJJ+8id/UtIW6yBta+upT32qpC2OyevKf6Vtn25Ux4wXY+Rzg0wQ\nm8hced38TT0uE9B9877g8cCcdVhyfO9j7IjbIrm2JH32s5+VJP3hP/yHJW0WGd9fuT+pvf3vJm9O\nHXzawPrm1kTWE/urjwHzTrwPsTLSZpFpFoTsM/TU0mECXbdKJF14o5JfxUg2uvG0dKws1m7B5czi\nXPP3E94vk9Ld/25nbVqvWh+4xq0wQ788GAwGg8FgMBgMfk1jPmQGg8FgMBgMBoPBucOZcC1rLlgZ\nPOpBTulO0gLCM0hKOgxkcvccAv7e+MY3Stp3qbjzzjv3/nWTF3Xg5uGmX35rlI1JuUdgmSS94AUv\nkLTRlfq44MrQAoMJVG/jsnIJy7LmWtaoTB8PNzPvQ2ZO9nlATjJgXtrcClYBZY2QAZcfggShY3as\nAvqbiwlobcG83bJwI7O0s/UdVzEPamQ8kJMWLPiWt7xF0uY6IEnXXXedpO56w9/p4ul9PwYyCNuf\nh0y460ZmHG8uaatgceDPY6ydAADgjvHJT35S0n6gZNblLqhJVdvGsFHkMs/c73Obbo2N0rkRedA/\n9henI2c98BynZsblBnnz4NJ77rnnoD+nhRZ0y98uCxdCCzJmT3C3CgD1KOvC54r9F5ny5/Mb9xHE\n7X0gLUD2Qzrca/25SXDicsD5xFy562Xu6V4nc839LUCbPrGPNDpWgpz9nEK2vJ2nibaWeWZzY2cf\nw8XsL/7FvyhJ+gf/4B/srsFdFFck3I6k7ZzG/czdBlk/zLe7jzIXrD9fazfccIOkbd+/9957Je3L\nFH3B/czfZ3CHxaWUvUnazsMWJM6e9c//+T+XtH/mfe/3fq+kjVYcGXF3IGSpBWonOYQ/1131ThsQ\nbbh8M3b012UXWeEd0IkhnvGMZ0ja9vjmos8YuLsa9OuN8CTXodfZQitA2/uyLe3dI9M8uAs07q3s\nfT7/+Y7U9tyG7F8jBuB+7+e4lg0Gg8FgMBgMBoNf0/iGxyOp4WAwGAwGg8FgMBicJsYiMxgMBoPB\nYDAYDM4d5kNmMBgMBoPBYDAYnDvMh8xgMBgMBoPBYDA4d5gPmcFgMBgMBoPBYHDuMB8yg8FgMBgM\nBoPB4NxhPmQGg8FgMBgMBoPBucN8yAwGg8FgMBgMBoNzh/mQGQwGg8FgMBgMBucO8yEzGAwGg8Fg\nMBgMzh3mQ2YwGAwGg8FgMBicO8yHzGAwGAwGg8FgMDh3mA+ZwWAwGAwGg8FgcO4wHzKDwWAwGAwG\ng8Hg3GE+ZAaDwWAwGAwGg8G5w3zIDAaDwWAwGAwGg3OH+ZAZDAaDwWAwGAwG5w7zITMYDAaDwWAw\nGAzOHeZDZjAYDAaDwWAwGJw7zIfMYDAYDAaDwWAwOHeYD5nBYDAYDAaDwWBw7jAfMoPBYDAYDAaD\nweDc4Tf8/90ASbrzzju/Kkn/+3//791vv/W3/lZJ0m/8jb9RkvQ7fsfv2JX93//7fyVJ/+f//J+D\nur761a9Kkn79r//1e/96/b/tt/02SdKv+3W/7uA+nvf//t//25X95//8nyVJv/23//YL1kn7vuEb\nvuGgzt/wG37D3rV+nV+f+M2/+TdLkn7mZ35m99udd965V8ZYSNL//J//c699XONtaP9/85vfLGnr\ns/ePMeJfH3Oue+SRRy7cia8TX2Xw9n/ba8vf+lt/a1fGGP23//bfJPVxAb/pN/2m3d833nijJOnb\nvu3bJEnf+q3fuitDFuiny8T/+l//S5L0P/4AjqPFAAAgAElEQVTH/5AkfexjH9uVfehDH5Ik3XPP\nPZKk//Jf/suujLYz/l7nf/pP/0nSJhM+D8wlY+DzQB20hXY3uLzRFn5z+fwtv+W3SNrGkfWY1yW4\n/uGHHz51mXjPe97zVUn61V/91d1vH//4xyVt6495kbZ1ztjde++9u7J//+///V57vX+XX365JOkJ\nT3iCpG0spG38kSmfB8pog88DY00Zcup18K/LBGBu/+N//I+73/77f//ve9d4W7x+advDpK3v3O/y\n2dqedfze3/t7JUk333zzruxJT3qSpMP91397y1ve8rjsE+Wag7+Zd8aaMZGkBx98UNImE75mvvjF\nL0raZKvt34yrny/Mzb/5N/9G0v6edMkll0ja9gSu9TFkPtq58bt/9+/eu9/3vjwHXZZzb/fnXei5\nXjd/I5u+hsC//bf/9qC//P3iF7/41OVBkn7wB3/wq9L+Osq+uUwwbozDpz/9aUnS/fffv7sG2eca\n7yu/0S9/rsuAtN4vftfv+l27shtuuEGS9NznPleS9MQnPlHS/tmV55O3iXeV3/k7f6ek/XnPsfA2\n5lh4X/h79U4Ampzmmfkf/sN/2JU9/PDDkqQ3vvGNpy4Tr3rVq74qSe9617t2v11xxRV77fP3Icb6\n3/27fydJuvvuu3dlT3nKUyRte+BnP/vZXRnjQ90veclLdmXPfOYzJUl/4A/8AUnbfilJ3/iN3yhp\nO6d8zJER5s/ninmkDz4PrM2UMf8790Jp27uQd58jxuOuu+6StL9n/vIv/7Ik6Utf+tLB8zhP2Ttd\nTp/85Cfvtf2P//E/vit7+ctfLkm6+eabv6ZMnIkPGSapnUkMCC950rY4cwORDl/Y28bLJPlhjVDk\nJEubgPE8f5HLFxTfaBCw9oGQB2n7OKIvV1999a7s9ttv32uf9z0P1dZ3+tzGOjddb19uYo8X2mHE\nC+m73/3uXRmbY5s//mYef8/v+T27suuuu06S9M3f/M2S+kEBfHwpYzye9axnHVxHGXPm/WGO28dm\nm5v8uGmbHW33DY3f2stxvnS6vDCOtMnljDr5zcdp9ZHzWPEt3/ItkqRXvOIVu98eeOABSdsG7OPJ\nOv+VX/kVSft7CG1n7/l9v+/37cp4McyXOGkbM/rpZfzWxpM5yZe/Bp8//ub6tt7/63/9r5L255g5\n4j5ft7nf+qGTSqL2QcI4fuUrX9mV8eLE+Ps8tBfkYyLXj/8/911k5POf//zuGj6Uv+mbvknSvtyk\nwssP5dwvfD5cFqRtzqRtrJgX6mmKrzzfvKzJXa7xtg+0F6EL7X3+/JxXl0361D5uTvDt+ZjQ+pjw\n9ZCKHF7Q/OOf6+mX9531k2dlPsev9TquvfZaSduLm7SdJ7z4tucyX7TbP1ZY/zy/vetk/x3Mkc9V\n7oft3SWVjasXb98Dve2njfe9732Sto8PaVN8PfLII5K2DxRp28t48faxY22h/PG55oPkla98pSTp\nec973q6MDxfOFm9LzkdTgDXFfFO6J9ocIUusV59/1jfX0F6vi34yTtJ2NnMmeJ2Usa/6Oxbgt1/6\npV/a/dYUZhfCuJYNBoPBYDAYDAaDc4f5kBkMBoPBYDAYDAbnDmfCtQy3EDdDY9bFF9lNnOmL6uZu\n6koXAOnQDcxNcmk25bleljEF0mZqpM7mb9riGvg7TbFeP33BNCdtblGPPvro3v3Z13weSPOu19H8\n20FzLUt3iWOguSHccsstkjb/a0fOsXRotr700kt3f+O/yTUuS+kW4PKSvsQew3XTTTdJ2uTZXUQ+\n8YlPSNrmyucIl0lkf2Wy9z4RD9HibpgjTMXN9YvrvU5cX1axALRv5ZJymnjve98raYs9kraYpi98\n4QuS9mNDMP/TF1zMpG2+8IfGtVA6dIfx/rH3MK7NdaKtC9womMcWc8S/zcWvuaIxJ8ytzxHXpyuV\ntHYto6+MY3OxZVw9Vok11eo8pmsZz1u5x6zci3EpYz/1a3A78fuRE9aq923ls874E3fl8U05/82N\nhLWNC0ZzMeO35vYE2n7azqCTXEM/adMqHtXbe+xzo/Vj5W5I29gniENrsg98XHPdtnGgbj/Lict8\n2cteJmn/XEpZajEn6W7W4h/bfnwSOc16/PocXx+nXIdtLpoMe3zQaYN5Yc1K27sD7eMckKSHHnpI\nUnfL9XhCaf89EZdnXKFY69L23oY7VnMDzLgW6dAl1NuSY9vK2jmVdblLaLoUuxsYZyb98vevK6+8\nUtLm9u/nMGczbpLu2p/7mL+ffPnLX9ZJMRaZwWAwGAwGg8FgcO5wJiwyGQAtbZoENFf+pc/XXiMJ\n4PpmrUEL2oL2+XLlC9TZgfiN+5sGgrqadprntS/tZuVJjYlr+y+77DJJnQEitU4rzVfrQwvwOwm7\n2jHQ5g8WmQ984AMH16f2xDUQzAMaLFhFpE070LREOR5eRvty7LyMIE4PmKNdsG15O2kDgeeu8eY5\njUmJdYC2qFnNWtBfBos24gG0NV6WxBGunTymnECa8Pt//+/f/YaGnOc6kwoMKvTPxwUCDSw6jYWI\n+1qAfQb9NzQNabOQILvsXT6eaQHysmyn15lkBE0jR1kLtOUaD0pPJknGV9rYZ1hbvlaOSQAB2vrj\nX98HscQgS/ShnRNocNEkSp3xMtvQLOGANe7rKWWpaWaZd+5zTWlanlowLXCZzLXqz8v+tX0u99pW\nd7PGNQKA08RJtNctIB9SB5iafF2k9cJlOvfW9l7xute9TpL0hje8YVd2zTXXSFpbvfP5jmQWa1Y6\n0DxT6Esbp/YucJK2pSx4Ge1s1j0PHD9tML5tf4TQA0YuabPOJtupdGgZf+ELX7j7G7IGzieC4qU1\nkc/KIppWz5VlxddaI54AyZzq8prvpY1sif75WYRF66qrrpK0T3DE2DpTG+CdhbHyvdPJZL4WxiIz\nGAwGg8FgMBgMzh3OlEXGNWdoEFpeEP6mzLVbfLnylelf0HxBNo1QarNW1pPmF9/iIVKr6RqTpHRu\nsRlNq3H99ddLkt7//vcftOUkWqdmHUqNwIr+t1mOHi8QX5J5MKStryttKF/9aGGkw9iYk2i+WpnP\nX9bhvs9/6k/9qb1rPP9M5hNwywNajZZzIP1ofVxS+9Y0c22+GRdkt1EJtzE7pvYdy6Svd7Q2aFPv\nuOOOXRn9Y6xcI0RsFNYv3ycyD8gql0Kb92adTX9kt3Sklq9pOJuWmXlr2rfUILfcIs06lDSzLZaD\neXDrF/lR0My5H/TjQdm+olh2v3aoWLGIIje+nl7zmtfs/dY03W0vOAntb/M+AGnFaH1qlOecPRkX\nJa0tgbl/N4110rc3jfBqX2w4JtWu1M+ljKVqlgLeJ1oetlzTbYy5xmMjfuAHfkCS9OpXv1pSjw9Y\nWfWz3Ssa/eZJgZw0T4O8tpU1mv+MP15ZbVqfmoX8mLGV7Z2A52Gt8Ri5jDX18UlrJ14X0mblb/sG\nfW3W0uYtAfLdcxUb12SSfra4y1X+nxanx9/s/56ygDp5PyXfobRZwbnGqe7Zz6jTY2v8jPxaGIvM\nYDAYDAaDwWAwOHeYD5nBYDAYDAaDwWBw7nAmXMsa7SjmqDSNS2tXKFwhMOG5GRszFnV7ED3BZpjp\nPPPqKjsypjDMdG46TOpUN1FS1gLRaAvXe99xj8Gs524TSePXkCZrB/c1M3QL6FwFlT5WMNaeXfnD\nH/7w3m8ejJfkEC2IHhcMd9lCPlYuZSta6lWgXqMlxe3gjW98o6R9+sbbbrtN0kY04ZSQgL67WThl\n3ed/5baUJulW1gICUy7dLeCYrmWMldNoQnqBm5O7OxGkzVrz+wDj6TLPOHLfKgi3rbV0vZO2fQIX\nLAfPY64azXcL6E1XD5/rdBtorjTUtSJr8Oele6KX4eJHP48ZvOtobsn8zXh44Oldd90laWs7ZBGe\nPdrdf6R1FvhVpnsfyyRy8L0z3QdbsH+6eDkZTQYEO5iz5r6SctDchvNMaO6LqyDlFvR/kgDyx4KV\na3Tby9O1rPU1STeaa/Zzn/tcSdIP//AP78qe/exn79XZ3EZPghaYny6IrU1JOOR9yBQC0qG7YCNw\nSPe25prWXL2znY9XKgfe5ZoMQuzgwf68W7VQBNrMe5e7EaZLWXPLAs3FswXt53MdeV0LM1i5uzZ3\n0Sxr5w1l/u7Cfs87i7+7cMZCrOJlrCsor729n/rUpy7Y9sRYZAaDwWAwGAwGg8G5w5mwyKCtcs1u\nWhfceoLmJGmYpUMKRA/Yo37+bVoxntcCpvnXtY2pXXCtKO1q2km+RNtX+CrgDu0CwcoeLJ501E17\n056RmvkV/XLTyB0TJFOSpM997nOSekJT0ChImW8sMU6HvLK2gFUyyBbwuqIlzeRc3/d937crg4Tg\nHe94h6R9LRH0tjzX5Yy+IhuuYc8gX7eYJKWjW7iSBME1a5m4z+V7RQH6WEH/PvjBD+5+y3lz7Rlr\nODWt0qGV1PcQAsBX66ElW0xrZ9PoNgts9sH/n4k0G/FA+39Shnsfss6mGV3RhPIcX2OMMUnS3Pp1\nTMttC2JHy8q/WO2k7RxBlp761KdK2p9/6mqJRjMJqSMtFG3/JckmbZM2DTBtou5GFsHa9jWeQbG+\nBjK58kpL3IgkVtT8mSZgtU6aVetYWBFjNC8HypLYxPuTtMt+hnzHd3yHJOl7v/d7Je0nvcwkmSvt\n90qj/vXuq82jZYUVbTbIRMhtPlfkECuSkWOA/cet9exPpDjw9YS1s1kekBGstk7NznNWxEgr62dD\nvh+uLGuOHGs/95m3Fuy/spbmvubv64wR/7KvSts7HG3xvRYrFmXuLeOJr78WxiIzGAwGg8FgMBgM\nzh3OrEUm/Tf96z3jGlzbkP7Cfh+aqpbULH2JXWPm1iB/hnT4pdw0n/hTej2p1fCv8qQnbD7s0P65\n/3dqOr5eLVjTrLX4oJYw6rThdLrMSdNKIhOp+fC/8YdvFoSVVmlF2dk0WM2/OJ9H270tJEtDM/u2\nt71tV/bwww9Lkp7ylKdI2jS70jYu9M99/NGQt6SJGePSqHYzVsbrQHZ9Ho5Jo3nddddJkr785S/v\nfkNDxvh4W9Kqy1hI2xjxr8ditdgfkDFAK8rMZp1t9WQck481bciEr9JaBlPb1qw1TUu7inugjhY7\nxF7Q1l/27zRBXzxWEH9rytwnGxm44YYbJG3Wj0b939Z9/tbiEtr+S51XXnmlJOmf/tN/uisj0S/t\nRMvrtO1p3cVqKG3zgUx5mzKeaZXQ0tHivPLak9Lufq1rTxur+IC0IkmHMVUt2S17I3vQ93//9+/K\nXvziF0va19zn89paW+0zLW4xsZI3sLKagFWSVEfW32QKWWyW/OzTivb5NNHOqXyH8HeZtN659Y2z\nFstBo9NuMgZaPFv2fZWgtL3rXuj/7X7p0HOgxRc2uV15DqRnA+8p0tZX1lDrA+PvlM4Xk95jLDKD\nwWAwGAwGg8Hg3GE+ZAaDwWAwGAwGg8G5w5lwLQNuhiTgCtOYu2bgDpAuANIhlbO77mCqaplNuZ6y\nZkJvgXO0izatTIbuvoJJckW5hynOzZ78hvuBj8tJAvpWLiPtGv7GpOruccfM0Eyg+4MPPrj7DfeR\ndGNxMP4+D8gH5mAP1G3uiVm2yrx8EpN9C9Br7gTU/7KXvUzS/vjeeuutkjYXqm/8xm/cleUcuXkW\neaGsZaJvWYfTXaG5ztHeRgV9DECd69l/kQWCNr2MgE7a5y5OuIFwva8Brm9kIhlE2dZTC2YFK7cu\nxq65dq7cRlb0q60t6Xa2Copu7gMt2B8XP+ryfcLdoE4b7NfQP0tbn9lvvQ/pUgZaP1s27RWdae4F\njaaffd8zgv/0T/+0pEOyFscVV1whaZNbH/tf+ZVfkbSNeSPNaa7WuRc1V590B2r7R3ODyrF4PF3L\nwCrQ3MePPRIZpu3urgiN8l/5K39FknTNNdcc1N2yoed6cvemlUtRunKvKKWbTCYJTZs34HvRxbhH\nXagfWeeFfmvufccA+4A/g3OjjW8STLlrKlnpId9pRFPNxRA0yuu83+XnQtTs0tqNO11CnQCEMs5D\ndynPdyrfJ1ckAdxHm3wMcJUn9YDfz9nA+Dut/MVgLDKDwWAwGAwGg8Hg3OFMWGT4KnNtKuALr1EY\nt6/a1C57gO8qSJmv1GZlSM1VSw7IF2XT9vPl6/ehEeD6FohGe90iQzvREnjiToKg/es729K0NxlI\n2oL3/uyf/bN7z5ek++6776Cu0wJf5v4M2pJf/96uJG1woGFp2teTJD08KUVkBtg1TXlq6hzIBAnW\nvM6PfvSjkvYtVWg/miYRqwQytNII+nhmckYvS81MI0E4Bkio5XSzaJNY5x58mbS0JJN1NFpjkIGI\nfj3w9Z5a1JaYtGkvM+loo2amrhaY2f6fZadBf4sMtDVGv5r15ZgEEFhisFRKh+QxrlFNq/oqQHZF\no9yswbkeGo02Mulr+xd+4RckSX/v7/09SdIP/dAPSdoPMobA4LLLLpO00ZZ6X9B4OvU1VuiWoBAw\nTs2SkGQ07X7Qkue1/fWYVLuORu/b6PKx/mcCa6xgkvSjP/qjkqSnPe1pe9dK23nbaJtPYlFpaxus\nLBU5xm5xXtEo5/m0stK352VfWpLslTUaHPOscLCv+tmQVpomn7wvYMWVpI985COSpKuvvlrSfkLM\nXGO+761kPglS/NokVFh5ADRLfktenGt5lYZklfB51QcH45g059J2tjI3fn5cjBV3LDKDwWAwGAwG\ng8Hg3OFMWGSAf6EnLW7zN0YT3bQO3Icvo7R94aFN8ZgVtDJos/3Ll+tWPsiN8pGyprlGe7KK82kU\nmGiA+RfqR0l661vfutdPrzO/lFtiS57nX+Ek+0KD+N73vndXdvfdd+tYwO/bqXZTY900vZm4TNo0\nlGgxV9rBlnAwx8fvW1lWQKPrbomqUhvmmlVoP9Fc+NjzN9ZE4kWkTc64z8tOEhvVEssiS2hTXF5c\nU3naePTRRw+ewd9ocpp/MvPmmiDuY8yan3hLFLryeU9tVtNctZij/4+9Nwu27qyqv4d9/0fFni6N\nISGEdARCACEKJER6FCyVsrfsSq/UsmyqqLLUG28sLS3lQktUQC1U2hQBAgQSgpEETEJCEwiKgmLf\nt3wXX/32Gnvs8S7Om5z9eg41x83ZZ6+113qa+cxnrdmMyRw16xf3Zh78fmsF5rKda9bP5q1pMfJ5\nLb8ma5EiZg996EM3x5qH+LCA3nZLHjKAtdTnIXXHGrV6swimR8bnMT32bunkM+10jwqFFCn0+qIX\nvUiS9P3f//2bc5gjZNjHlLXJPuUykjmja3kjjrQAZxFc/7xmbW4W/lNliV9r15q1l/F71rOetfnu\n4osvlrSsRx9/xqZ5XTPKYW2f8D06vZ6NRjc9441am754cWVkEXlt65815HtQemRO5KGRuoWetuRz\nUbbhsAGNOfuH37vJIn2nnIHnQ11yySWSpFe+8pWStr2mF1100db5FLiWljW6liMJ1vKZmvw02cpn\n4rYXtdITqS8aVXJ7/qJ/5OL6/Xmmbjmg+czpzydr3t/EeGQGg8FgMBgMBoPBscO8yAwGg8FgMBgM\nBoNjhyMRWtZcTmuhUKC5nnD1EUrjyfucjxsTCmNpCUMgydxDfjIcxF33uJpxs3kfMsTAXXG420hM\nddcq1+ecRrVLEryTIFBNlaTPk6XCZYw9LOSKK66QtLhlzzjjjM2xRs5wWLj11lsl9cSwDOfzY8Bd\nomeddZakRTYa3eyam7yFR+T5fmwtGX6NVKKFbwDkGbe309viAv/whz8saTvx+T3veY+kJezBQ1qQ\n9RbCiKxm1XDva6venPNwmMj16+2kXy7z9LlVb+Y7ZNhDKLgP56+RivjcJpWzJ9+ukQpkZXG/ZoYL\nue7J8KYWHtfkmmNroUXZTz+vrQc+r4XT7QMQQPh9Celo+om+ZwhkS1LNKtd5H6kn9HK+jznrlaR9\nX6MXXHCBJOnKK6+UtNAxX3fddZtzHvWoR21d57TTTjthm5qeor/e3gyZ3SdFciNM2Bdapftcd95X\nCHMIj0GXPPKRj9ycgywR8uxkK7fddpukZa/y/iGLzJ+HG2VbXHclDXILH01ClrYHsY+SpC5th9RL\nPdQa+Tr77LM3xx74wAdKWuj9s/SEt3ttzdNP31P2GW74vve9T9K2Pk4d6HNG/9hXPVz5G77hGyQt\npQDOP//8zTEIAPh9W2vAj7Uwc5DPCa6r1yiy14gV8j7e9zzm106yF9eZGVbt12HtQDzS+s6zruuy\nkwlTH4/MYDAYDAaDwWAwOHY4Eh4Z4G/MvI0lNam0m4Tt1mne9hsVMRaTlnDJNbDMtiJoLaGfa/FG\n2SwLvKG3AlAkR7k1BosJ3hpPZOW7TKCS1r0KSafpb8VY5n/4h39Y0pLQJi0WKKxWTu1LAuQ+kJ4E\naekDc+pjjfWcc3j7l5ZEeawbB6GG9O/SQu/fNetieioarela4c5MsG39c9pHxgPPHZ45aSG7gLbZ\nZQlZh7rY75dFZ73vWRzPrSj7tOoyjljhpaXNzLfLC5agRkWaFsPWbsbVz0X+WaNO745HDEteKzCY\ntJjSbkG+tXl30D8vaAbSW7NW2K4lw7eE92xDo2nH4uye4n0Wu2NunGKZ8WgevDVPKshxWfMgtL4l\nDbe0rHOsu05iwjr8nu/5HknLWn33u9+9OQfLL952ZEza9dY1IpdGTtHamUiK3pZQvoa2J91T+u97\ngzX6ZfZ+vN601b1md911l6Rl3rxwXxKHeGI9uor7XXrppZtjrBFko5VZIIqgtTv3J1/H6BL0oa9H\nroEOc32DTNx9992StveLP/3TP5UkPeABD5C0jJc/R/GcwJi2ZzPu3wgT9oHm6U4vpnvkIQegfa5f\n6Tt7rK/RG2+8UdJC89+89Y0Sns+Mo+9hfF4rstl0UBJQOHJv8LWZdM1rRADuiUvPlkfSQBnP2vE9\nM6MRHEO/PBgMBoPBYDAYDD6hcSQ8MryNtTe8jBWVFssFb//+Rnr77bdL6sV+sLBgVW3UgFgu2hsi\n1oUWT83btPeBe/O23/qAR8bvd+2110paLLzeB6wgeBn8zT4t0A0t7ptrQvt58803b46lZcVjs/dZ\n6I64Vo9zT2uBW0roD+OK5URaPBUZxynt0iH6PGShKfdGrXngkCGsaT5/mWfQKBORXe8f90OWWs5K\n5q5Ii+X2cY97nKTF2ist1l367FbGLNja8gQaBe3JUCaeLD74wQ9K2qbRTM+Rg7ZgWXOrG+OHDnEa\nTdYk68+t57QBS6vLEvNM+7wAZ7azeV2YB5fBHE9fD+gzLKvNEo8suPWT+aK9je6z6Tiwlu/zlV/5\nlZJOHd1uyzlIy2GzYmfh5DUa9DY+LU4813SjdEZfuwyTb0Eu30/+5E9Kkn7lV35lp0/Ihuc5IK/I\nRqNMBWuFUX0dp0fuIPTyrQBlK7K8T6+t46Byx3nkgNBmL8aMnGGtd32RURwuS1ns273J7Klcyz0b\nXIu5bOsxZdH1N/LBuvdcl+y3P7Ng+ec79wSkXLdyDfQXq7vneOA1p02+L3obDhtr3ujmmcWDQNtZ\nX9LSV9avexeYd55B2V+lxUuH/vdC5jwvkJt84YUXbo6Rx32QvbaVQWBvcVpj5iijnvyayGKLJuGa\nzeNMG1yWoaPGS+PjmbJ0T72145EZDAaDwWAwGAwGxw7zIjMYDAaDwWAwGAyOHY5UaFmr9NoSNnHl\n8juqSUvrCe+ZGOxuzw984AOSFppCbwtuM1xjnsCGWy4p+6TFfch93J2I642wgne84x07fSCh293Y\nuOlwcX/kIx/ZHMO1zP1aMuZaxVZCadyd+KQnPUnS4qr20DLCv/YBxsfbiQu2ubQzoc/bibysJTA3\nGux0d3v4EmOMm7VVmycczOcvw+NalVvkzBPmOEaIgocfZbK3y2e296qrrtp8h8yRsOhtwf3MGHvf\nMxxvjb7xMIF73O/HGBEC4SFUjDHj4WNGuCEy5ToEognCBzxkg3szp64nGA/CDVx+0A8N/A5Z8vnk\n+q1CN+dxHw/VYBxahe2UJZfdpMJv9MutSjzrjvk/VQQQtMETpTMx2vVEhjy1hP78nY9Php15P9ua\nTjAvhOBJi7wRtkjY8Nd93ddtzkHXnnnmmZK2Q8uYR+auEaQg597PJJ5oxCP0ve2rmfTbSCMaTfm+\nsRZS1kLdQIaZeqmBHNu3vvWtm2PMTSsLgN4lfJu/fh/Ch1zHZnJ3Gz/6iQ70MCf099/93d9J2tYb\nEPjQF9dNtI9nJJeJpLqn3a5zkQHkzecCmU3Snvx8KpDj6SGqPFsxj66PGR9kwym6Wf+Mva9RrkE4\nsM8H9+F3HsrMuDD2bX/jmK/7JKPxaxIyyZ7X9huuTbilt5Oxc52Lzso2ScvzF399XNZCV09GZ4xH\nZjAYDAaDwWAwGBw7HAmPDFaf5pHhjd4tA1kUstHccS33gvDWjZXErR4kWmHJdyteWkfc6vzGN75R\n0uIZ8TdYrBItYS9peEkwk5a3dfrpVML0D++JJ40yjo2qLxN03VLCZ9rrSWpYAEjQapbLfYAx8HGh\nmFwD1ggs7V7sdI1mdK1wVEtqA1gc0hItLfOM5cFlCRnEyt+oXTNxTlqsJvzOLTP0mWu1QoW0z60o\nWTzWPTlQbTKuvv6QLzxOp6rYHW3x+2GNbEnt6enwOaLvWKw8GRKvKlZztyBhNeNa7q1hjhpRSCYz\nejs5P+lUpWW9NxrmtNI5mQFzibXcZZhx4T6uQ5LkoSXtN09ctnefpA8O5qEl9CdZh7Rr5WvEBeml\n8XWYZB3Nst8s83xu9PAAylsSwt3aCyALcG847US3+H3R2+iIRk7Q+pCEEM2Tn9bblhjcsG/vzBrZ\nwhrNPmAd+ffoibe//e2SpDe96U07xwCU95L0tKc9TdIip24ZJwqEyAtfv+gg1nYjEkiSFm8H8849\n8MJIi1eAteqRKbkXOJU/ZBToOWTQ98Hyh60AACAASURBVFrazTUbSQE6xufnVHhk1opdewTHQx7y\nEEnL/uZjxxy1PZN9MWm8pWVtMH+tnEjqZWkZK/YgX2MpE37NvJaPNe3jWZVnSWl5BkT/t3mhLd7O\nW265RdKig7yd7FPIqT+DpF68pxiPzGAwGAwGg8FgMDh2OBIeGd76nd42Czr52ybW+lYAjLc94jjd\n2pCUhv5GyXm8Ufo1iV3GikucsrTkF1AMya2bWHRpi8cN8iZKnKK/9ePd4Rx/u6Xtj3jEIyRtW2HS\n0tWsjIxjs6a2QoxYbx7+8IdLkl7wghdsjnlRxsMG4+GFvIhLveOOOyR17xAeOLd45lu/jwt9xTrl\neSnpDXRLJ79r9MRYrJgrL6yG1Ya/7ungfi1nDLlGFt1ThVw160YWW/S+sx6wRvm88zvi992KQl9b\nwbA12t57i5Yv5/IhbeeMoU9Y2x4LnjH/55133uYYFifGrMUlrxWjbFTQmY/gc5ReT593dFzLPcoi\nvu2+TdelLvBcnvSE+3ym/nRZSq+Ae6X2Sa3aPMwJ14snokhuxfJa4b6kGm66md97vxm7tkaRS3QB\nMuw0pRdddNFWn9wKTluQA7wv0iI/yLTLSMpLK6yXxWR97WX+ll/vIPlC+8Zaf1q7cm48NwovGaUJ\nXF54HsDb4jrpm7/5myUt68Lp76+//npJy/7g1MwXXHCBpN38pSanzSPjHmZpW39zPs9RrlugjH/O\nc54jaXmukaSbbrpJkvTgBz9Y0uKh8QgOdCbPdK0QZ8M+vXStcHO2yT0yPEPQdl//qRfdI08UA3lJ\nLcKIdeu6AQ8J37X8ZXR9e4Zs3saM4PB1y7xB89xys9gz/ZrsefTPZQzZw7vj/WMdeekAsKYfpiDm\nYDAYDAaDwWAw+ITGvMgMBoPBYDAYDAaDY4cjEVqGG6uFO/Gdh/xk6I0n4+IOzIRPaSEHyKqn0hIS\nhpvNKXNx/ROq4glehAFwX0LNpMXVT9KmEw9wDfruJAFck/Y6DeSll14qqSc3r4X1JJWou/4yYd37\nd80110iSHvvYx0qSHvOYx2yOOQXlYaO5bukDIXuEmEm7SbReWRbggm2hO8ytuz+T2rVVTiccwGWJ\necdNS4Ku9yuTOKXd8CEPWyLMhDa4Ox+Z5a/PLe1i/ZCs7tdP6mppcXcnYYWf30KafBwOGy3ZP13g\nrguyLS7X9JW17QmPhIhkeIa0q4/8fhxj/JtrnLHysEyu5ZScAFnlr6/3pJVu67/JdSasu9xlBWm/\nZtJv+npgHtCbjZxln/B7JKWnz8OJ2tIo2RmnphOAj0+Suvgewrw1anx0EGNOwrTvebk3Os0/1+Y6\nvlaRxUb6kO125HlroT9trwX0s1F17wsHoXRdC1vJkDlpmTdCplznsX4IEfNngDe/+c2SpO/6ru+S\ntF21nd+9/vWv32kDa5t1hEy5Tsvwe28Tzwysf54lpIWMoIUb8/kJT3iCJOl5z3ve5tjVV18tSXrF\nK14haUn297AsPiOTjXCBdvr+tk+ZINTS1zH7IG0hwV/aDftuIVvsER4qmLTUv/d7v7c5xjwQIvrk\nJz95c4x9ptGYJ+GTjxM6oelj9iXkxfUxxyCCaCQdqTek5fk1dZmfRwilzy16sJUgWFuPQ788GAwG\ng8FgMBgMPqFxJDwyWN0bXV2jduTtD4tCK/KFNcXfmLF48kbpyXUkK/FW/exnP3tzDKs0Vlu37HLN\nRvF71113SVqond3yiSWAJPrbb799c+ypT32qpCXR2i0teGSwmL/4xS/eHFuzRGYiarNS0odGV/pT\nP/VTkqQrr7xyc4yExH2gJTcz/rSPpDVpGT8sAgdNMqTPzGkrcNeugwwiL249wPqBx8KvSb+wWLRi\nkmuWzUzGkxaLHJ68VoiLtjeaaNrg7VyjUeZ3jRp9nwQQwPvOOs8x989YUd2bwbxhmXM6W4gmmAcf\nsyxa6W1JHeAyyNjSzlZ8kPNd7jLJunm/sHi53OCxa8Vj18C9W4JsFl5zKzvrhz6cKkruRuCRerBR\nDqe1z63v/L55uJi3JNiQdolD3KOHvK2RrSSBgBdPBI0GGRnkvk4SkHrGLfrZlkY8Qj/XyGHWSCYa\nacSpKo7p90mZaMcS7vVCr9FXp1FmPZAE77JIpAeRDS4TPPc84AEPkLTt2TjRuDfSDs7xiA+uhV7z\nvZL2IS9ONIRM8DzkpDJEY7DWKR7unuQsxOjth0igUdDvkxCE+3nUC9EceKecwCn1cQO/9/UEsQOE\nPO4B4rmE7/w5CgIJvDYuj1CyAx+z1MdOsIL+55o+viTr8yzhURp/+Id/KGmZR5cb9hnk9eyzz94c\nSyIF3xfRExQaveGGG5RoXqVJ9h8MBoPBYDAYDAaf0DgSHhmsRm4RSguWW314U8by5G+ivAkSE+qW\nEywWWGg9hpm3aCwIvD1Ki1WCdvrvHvawh0laPD/+JspnrH2NujIphb3PT3nKUyRtv/Xzhs1bsY8L\nb7PcpxUvWysS1+LH8xjxsf7dz//8z+/87t6iWcOZGyxZPu/Q52L58HFZs9AC5M2toBnf7VaHpDz0\ntqwVDsRLwHduCaavzWroXsDsH20nF6fF32MJ8nbi0WQ825ixLpzaE0sO6+Hcc8/dHGvFIA8LOQbS\n0ld0h48n88y4OC0tY4vlCu+ntEt/3orQMT5udSMGGP3Q8tfW8gDpg/8uc6Oa1wyLqo89nxuFNL9j\nvt2LBdBdrXhZ89agW1t+0EG9QfcEzXPLvVM2HOlF8JhurpnF4aRlXPjbLMmZ1yTtFmX13L8TXbvR\nGa/RGtNPvy/yjRw0D2J6WP36uf96m9Lz3zwtzWO2z0LKfs82Ro2uO63B6BmnuGbcWEfu/SBSAA+O\n6xl0AtEfri+ygKJ7DE6Uj9Y8pNzXvT2sf55j/L7vf//7JUlf/dVfLWl7nLg+coOFXlpklnVxySWX\nSNrWaexFyJSvD/I8GF8v3N28eocFZNj3BnQenhjXgYx9K62ALuM5wyMQGB/mw/OJb731VknSM57x\njK3rOPLa0jJva+uIc3yO2TcYYx9fxv+bvumbJEkvetGLNseS7tvn6OlPf7ok6YUvfKGk7XIpeCP5\nXaNfZlzc0+V5gNJ22YxWNPhEGI/MYDAYDAaDwWAwOHaYF5nBYDAYDAaDwWBw7HAkQssysVBaXKO4\nwRq9Jq44kuqlxaWG+4pkLmkJ4yIRzt100OKRkOb0vbhZSdr3duLObbTNuMZws3poTLr1zznnnM0x\nwswIGfFQKK6Ja9sTEp1iUeoJjS20LBPBWyIyf90F627HfaG5YBkPpyBOV7HLC/1roQ8ZjtfcyLhG\nW7gM3/nvMnyoUV3z18eaayL7Lp+chwx5SE2Ggfi8ZHViv2ZST7ubl/sRmulJh4TJILPeFihI9wHm\n3UMEGMdGYpGU3B5eRRgAa81DUAn1yBAsaQkRIRSpJaw2Cmoow5lb70MSOTgy8dwTM7k3oQgensp4\nIC+tOj3z5vOeYamue/jcyESScMBl/lSEljV63wzPkXZDtJgrJ8jIcA4fA2ShhS1liKC3ifCjpmNp\nQxKytDXO/Vt4LOe4HNEv1r3va0lc4DKS/WskA8xxo1hew76T/ddC3NbC4Ohbo5UntAxd4iFe7MGN\nLIfzkCkP7aMNhKL53GT4XduDmOcWMs1zEPPv96W97Zkl147LhJMR+DEPFcoSAK4HOB+5a7S/+wCp\nBCS5O9DLLcytEbOwVnkWdKIDngWZz+c+97mbY1/3dV+3dR8nd2JuCVPz/SPDW9u+kaHs/pn59OdE\n9jrCqT2EMu9z8cUXb44RPgYBgJNEQF/dwgaRfe7jz8H0lfn3UD3KlhwE45EZDAaDwWAwGAwGxw5H\nyiPjCZe8BWOl8AQo3hZ5w3OLABZLznerFtZUEpj9zZC3dt5I/e02rTmNbpA3SX/zpX1pBZR2qVq5\nv7R4W9LSLi1WjSwOKS20do3KDsvQWqLlWnGiZsnaZ9JmS27DUsJ4uLUHrwLj4lYmrFOtD1lwzK1E\nWLFasmBap5sHiGu3BL1mLU6vkB9LyspGZ9qSdkHrQ8q1/441hQz7WCPjXMstM27BO2w0yxNjRlKt\nz1/SE7tljT5jSfJ1hJyxZtxChtUNHeAJ9lkQ0wuhukU0+5CW7YZGIYqO475ecIx2oZ9aITX618hS\nkCVvU1rwXS9hdeN8l7N90i9nMUn/TJ/dsp6kJxA73HnnnZtzkP1Mvpd2C0s2Kyhj514+9h7mz6+Z\nHpFG7bymN7JPPi9pYfW9izXAGvf10dqQ105P2xrN9T2lVb0nOAj5gCPPy7UqLX1jb3Z5Y401Glzu\nx3OMP3NwTTxArmMZ9+xL031cxy3rXBPd7P32Z6nsS1IPu5cImci9yPtEH1px5lZgEiR972GCkhWv\ne93rNt8hjx6xA9aK3jIerdAk3zV66ZQxj2zJKAsfHzyArF/XZcxbeybI50TvE5FJeKjcC/IDP/AD\nkha96HvY9ddfv9VeL+7K/diDvA9ZhN3HjM/IpOunSfYfDAaDwWAwGAwGn9A4Eh6ZZjkhxg5rqlvY\neRPNmFZpiX3n7datBVAWE6/crBu88ToNL/fG2+PUmbyx8kbq1hjekDPXQlreRFssOh6ERgmcBfmI\nW5SWt+G1fIFGTZkxzm0+GiXwPq0ozZLQrHsAKxr98rHGuuT0hCALDjareKOGTEtZy2dhTp2mMOPn\n21inZV9aPAHch1wuP79ZoNfyBZIW3C1zmddFDpm00HcmxaO033wIzw8B3Ju2e1uwUNImz6niPCxy\neGmlJWckKYylXc+fj2fqLKdhTQu+64k1L1bOresJZL7NX9KC+/2Yd9avjxm6pnlrMq/LPei0q9E2\n77PYXbNiZq6CtyW9zn/xF3+x9VdarO7IvlPt0vdmIec71sWNN9640853vetdkrYL8OHh5H6NQj4t\n9K578zs/llTpvoayWK7rvizO2jzPmQvWiuC1feOguTT3Fm3/y3GUdvea9Dz45+alw6LNOPrekzrW\n5YXxzzH2z5zf8u4yb8ot3TwToYs8dxEdlvmF0iKn9M+viUykx7JRuwPXV0ln38oZ7APkebjXlWfA\n9E55W5rniL62IrLpGfGxaHlBgPFgbbZnnuZlzDXWPLjMketxnisbVTJ03ciN733sDfl8431veWKZ\nA+9rhzVA+x7/+MdvjlF+5CAYj8xgMBgMBoPBYDA4dpgXmcFgMBgMBoPBYHDscCRCywj58fCxpAR2\nlzghVLjiGyVd0k76+SQpteT7lqyKGxK4ayxDMRpoCwmf0hL2gkuuXZNwIncrJx11C6Uhwc9djY12\nGWSy91oYwKlK9m/ItrRQmgy989/hEnd3aYY+NPd+o2FNt3yTJZLpfMyZb37X3MhtrvjdX/3VX0na\nlknCC9fmo4UvpMy2MDdczS67hCkQcuPhEo0W9rDQwkXpM2FjnpzImCETHkbAtQgpc8IQ+sP8ue7B\nBY6b3H/HGiZUyJNvCTcCHpKQISmuezjWiBy4JvLmIYx8RoZcv7TK8YB7Jy2m3yep4yXp9NNPl7SM\neSOV2Aca/XLSZrueyGR9woJuvvnmzTnve9/7tu7RdIknyALWD/fzkODbbrtNkvSOd7xDknTLLbds\njn3Lt3yLpGU9Mj8t+duT7fPYWiV7wqFJ4nVkuKS0hFwSotRCqrhvC3tKWXG47O8Da6HRoBGw0Fb6\n6PoxiRR8blgHjXY7CUOc5pvfNUptPqNvWthghqf7WGe4mc8bbUCvNZlCxzb9lrTEjUikEchwn6Qb\n9/P3AYhcPJwTqvpG7JDPGd42xrXRsKPzWhjZGjFGjr//jrYw7/7skm3w8DHkM9MwpN3QVZ4z/byk\nyvbPGcaf/TlRH9pexH3OPvtsSdLTn/70zTFPGfl4GI/MYDAYDAaDwWAwOHY4Eh4Z3ug9GTspi90K\ny1spb3Zu+eSNkvOxYEuL5Yk3YLc28JlzGk1l0o9Kyxsl1lt/i8y36fb2Tjv9GEnCLXEqi+b5WzjX\napb5bEPzuqxRArcEzX0mdreEu7SKtQKqzbrItbAEtWJNzaqZSXTNakQbfMyRz7/5m7+RtE2tzTyk\nB9A/N4siFhZkg6RhabEOZ3K7X6vRbyfFbitwiKejkSc0QoZ9IumwpV3rYJv3TESXFr3QinTh8WWs\nXYekZdQtpIwZ13JLNW1IelK/Buc0OWOMW/I9Osc9AMg49/H+MZeNCCBlz9uZSbpO95zeOf+dy85h\nI63p0i7NZ/NGYY1mnHzvIUkfL4oTquAFu+CCCyRt03bnenKCjCShcYs+ew3yw7l+DnPcvLVpDfU1\nnvPiex56ivu5lzcjEyAX8Xtxn/QaSLveqZb0/X+Btq8kWQZj1azfwC3Va3tk6tbmhWBtrpEl5Pft\nfo2CPJO+/Riy4B7nLB3QCv6m56l5t9B9zbvMd6cqooM15wUW3/KWt0ha70PTLc2LDVpx7ATHXOeu\nRfPkPtOeuVqZAPqVHmhp8cry3OB6Jgs9+++SDMPBMX7fvJmMj5NlIQt49CHkyv58PIxHZjAYDAaD\nwWAwGBw7HAmPTHvL5C2aNzy3TGDd4c3OrUyZO9JimYHHPmNpa5R0fMfbo7cl6Zrd8pH5LG7BpNhP\nK0LJNbCmNXpErEXNCrZG0dliV/P33r9G1wz2aVFplt1sg8sNViUsq261ZzyTdlbazbNqhduahSUt\nOW5hgcoVGXRrZBa28vvRH9rnVr+Mg/Y23XrrrZKkK664Yueaa1SSaSV0qz1ri3H1wmhp5VmzEh4m\nGr105hP4+uN8xtwtQVkwrFklOeZ9T6ttK9jaCtmCVmiyUTKDlDOX3fRMuzc4vWaN6rRZ99esg5mn\n4xTg3I/f+xydisK5Pu+sG+TZrcqMMfqednoO09133731O/fIQT3eCuJRaI7++h6CpZH8Mt+XmMdc\n237tzHHzeaIPWZBPkj7wgQ9IWvIiXO7If0NPeQFPvMmMYcvpY8zbHp0emUbtvi+0Pat5uU8E9hDG\nQNql+XavfnpdvH8ZReDPHMgHa8fHNiNSspSDf250v+lZad4a+uBrJ2nl1/antb0FWWyehzWdtA8Q\nVeAFHM8777ytc9z6n3kwPnaZk9eeBVteSps/kDrX5QB5aWPMvsY53of2/JT3Y89zeUUmmgeI8zhn\nLRes0f23/1kDPD/7eP7Gb/yGJOmyyy7b6UNiPDKDwWAwGAwGg8Hg2GFeZAaDwWAwGAwGg8Gxw5EI\nLWsJV7i0cPl7mAbnccyTXAnfwqXWQjhach1hJy0ciHvz10MVaAMJm63KLd95+AphINyvhZXgFm6U\ni7gTnZSAa7TQlkxgayFULSwoQ9H8d2tJbfcWzbWd9NB+LBMRD9q/rG7u85Du7lY5FzephyFwDcJJ\nvKI8cobMetgS88U5XhkcWuFzzjlH0racQXDQwl24JmPmMsF6IMnY+5DhLu6iTmKMfcqBIwkypN1w\nSB/rdKu3sKlWFTl/30IDWwJ7tsVDvbh36hJpGb+1pPgWEsr5GQ4iLfLcQh9P1N6GJvOcTyiVtEtt\n3pJE94GWTJ7z1uYPvUmI16Me9ajNOddcc42kJbSXv5L0nve8Z+u+fozPzDsEMNIyPugpD1dLcgja\n61S9rNEmP9yPOXbiAu775je/WdJCUiAtlOoQFjzsYQ/bHLv99tslrVPCZ5K7z3NW//a1uO/Qsnsb\nIs08eLgh16Lt/syR+3RLdG5zg7xkYn67H3Lj8841GwU5895C1/P3LWy/PZMlBXALB0t9s/YM4uO0\nT0IQxtxDPZ2II+/PZ8bQj6W+WSvhcVAyJK7BfXzMaLvv9yCfQXw8eT6kDd530Mh6WKcZYtbOb7IB\nGrFV2xcJbz3//PMlSTfccMPmGLr2IBiPzGAwGAwGg8FgMDh2OFIeGbfs8tafFmX/DkuIJznxuywI\n5OBt0d9S3TIudctnSyyFshKqTk8io82c71ZR2kXxvJYcu5bgxZi5dTqT/Js3A7RCjO1+ad3y8TyZ\nBMp7ikZm0Cwd6XVZo8L0Y2kJ8rnNwl3NAsE5bmGF0rcldjNfzavEeVzL+473g2RjyCKkxXKEFaZZ\nX4CPHb/D8tsKhTbCgzWa4LVk8XsL7ueFH2kX1lO3eDIP6Ak/xjw7UQjg+o1+M2WhFbHjHLeoZwJ3\nm3fmrXkX0HlN9pslL+eh0T03y2GzKoO0/K5Z+U5VkdSmK9cSh7PPUJE+7nGP25zzjd/4jZKk17zm\nNZK2ZQTPCAQbTv1PoTm8O94OLKp4P9gvpKUo69ve9jZJ0td+7ddKkk477bTNOewdeGt9v6JPjLN7\n6fGyXHvttZKkq6++enPsuc99rqRFp/g1oUOFuKTp3vS2rHlkmuV631gjtFkDcu56hmu1gq9JFNOi\nQJiTRlTS1kcShbRnJGSC+7t+a8QPea1Gu5/r18cw6dqbJzi9Ln7t/J2Pobf9sNHaAqU6e6bfP4tW\nt+T0Nq7psWjPdE32c65b6Q9kpf2e+7g+pj/0zz2Ia0XO876OLIDqssH9+K7JIuP4VV/1VZtjT3nK\nU7bahDdckq666qqdNpwI45EZDAaDwWAwGAwGxw5HwiPT3vDSAulvxeSacMypLDNfwC3XSdXq9+NY\no3j1nANp+22TwoS8UWLJkhYLV7N4YwlsMbcZD+lvzBlr78i4yJYn0mLt8418zdpwKrww0tK/Zg0H\nblmgX+RINCrQPNevz7i61ywLMDY6TyykLiNYZvmdx4SmVdvnAZlFFh7zmMdsjiFDrAOn8kaGkK9G\nDUq/3OPEWmE9NTrFRimascBrY32YwDLePGOsGb8/3i/Wha9p5oS+tJwVrtU8vpmf4m0hN8LHjPki\nb8rngXvzna9/7pfz7+1shS2TCtqRlupm5Ws5cbSzFRhdowffJ9r+kB7p5hnhL3rYrZnf9m3fJmkZ\neyiMpaXPjC+eGWmhd8WTcr/73W9zDL300Ic+VNL2Gn3rW98qafHWNNrXLGzoOoX5oE8ur9z3Oc95\njqTF2yQt+9MTnvAESdJ73/vezTFyCJK2ea1Ybysc+H9RELMVfQZtbwSZc9C8mKwxp1FOj1SzVING\n5d725NznW5RFlnfwqADyn/i97xdpWXcdtrY/pVewtSmfn1z/ZH6X5wzvM0cGD0fbw5JSXlqnQ075\nabLMMZefHGtfK8wj4+vPiTwLcI5fMz2xvm/QB+TUvcqs1zWP+lpUUKNm5rxWCDU9nI94xCM2x/Be\nv+lNb5K0TenvdNkfD+ORGQwGg8FgMBgMBscO8yIzGAwGg8FgMBgMjh2ORGjZGvUwcDcWLjFcXCTc\nS4vrlqRMd59lFW8S7aVdd7S75/mONpEcKS2u+8svv1xSp7KjLZ78C9UtbkHvL+EDjX45k1XdhZfJ\ndAdNdsywszUa5ntaLflkQRtasn8jJ2CsCOtx1z8u15bsn31tVc6RIb8m4/7Rj35U0hIy5MgQLP+u\nVehm/u573/vuHMsEez+Wbl13TSN7uKb9GPfLEAf/nO31z83tvE+0tcln2uK6hDWWIViOVmWaz6xX\nD4FgvttYcz/WnVN8Jv31PR07D+fi3oROuA5JQhO/H21vyaxrYVmAMBWnHl7THfvUE43KNUNDWh8I\n/2JuSWqXlrXy/d///ZKkX/7lX94cI5SM0C7CbKQlRIzfX3nllZtjhIgwZ9COSkt4MgnIhFe4nDPv\nLbQs4SEmf/mXfylpCeEgxEySXvKSl0haQjhcRmgn++lamFarLJ8hhu3YvtBCy1KeG634WuhyUty2\nUhFtbSdFvYeaZWiYh//Qdn7fdBfhia4TALrLw80Ae0I+Y3lf6J/LYBKB0La2Z/Ls46FQ+XzSyFf2\ngRbqm7T7bT4bcUnKyskSmayFVSJjvn6RifacyNg2uvEMM21lQfi99y/XwNq+7/tGUvO3PaURRhG2\nix71cNw1HZcYj8xgMBgMBoPBYDA4djgSHplGT5xWFX+T5bxGbwtawiRvs5kA7W1o4HwsEG7l4G2f\nvyTZ+Wfeqp1Wj8933nmnJOkhD3nI5tjZZ58tadeT4O1slgQsQmtFL9e8NGtel4MUzztMtGSzJIVo\nBc8YV7eiQ/jQ+oe1oFlWsE5g1fAEXSwJzUKa1uk2D83ak4X83KqRHqrmPeE+LmcQYSDrbmHLdeP3\nS49B8yBwrTW658MEc9qsZ1ggfR1jZceq7Im2eS1P9udajJnTsGbCq48118Rj4QQemVzqXoP0eq1R\nX7o+y4ROvEV+rFm1Ul5cJvJYWxckpbuVL5Oa29rcB1rCMv1phQLpFzJLIbabbrppcw6F2KBkft7z\nnrc59rM/+7OSOnkGxdyYd09cxTvXvBHnnnuupMULwv+OLNzr10kaXY80ePe73y1piRyAyECS3vnO\nd0qS3v72t0va9hIhS9BLrxWTbQn96YlZ84IfNhp5UFqaW2HnTEZvBbNbocckwvHnCnQHv/d1wmf2\nJy/AiSzwl7n1pH3ux/p37zDXbgWbAZ6Z9kzQIj64Pn3hvs1q35L3Gee2b3gZicMGc9B0LnDZzfFo\n0UCc733Pa64RSjQ93sqCJMmG61LGzCN9QD4n+jXZS9ZIodr9snBzK5bZyKi4Pvd12WBueFZ23XUy\nGI/MYDAYDAaDwWAwOHY4Eh4Z3t78rTFzAhpVMm+BbnXEYoVFwt8os/idW+2zDY3mDuB98Xb96Z/+\nqaRtqwqfsYp5PDVvvFjvPO+G3/F2CmWntFhB2pv9mtdlzSp6EItp82acCrRCdy3XhfPIl/rQhz60\nOXbGGWdIOljhT79mFjj0HBk+e0G8RLNGZjy1Wy5y/tZiyZuHcs270Dxqef7Jzm0WDJX2W/ywUVei\nCzI2288nB+FJT3rS5hhrPy2X0i7lsa9bPqMDXIdggSdfyj05SQ/t3hpkKde2tCsTTV4YD/8d7cs8\nL28DcMsac9py1LAGU/S3UcA32tV9Fklt65ZxRI9Chy7t6ka8dE4Jev3110uSbrnlFknSIx/5yM0x\nCri98IUv3Lq/twEPB94eaYn9hEWRbAAAIABJREFUxkPqY3fmmWdKWvYnLK2e+4lMNt2XXr4v//Iv\n3xzjGvz1gs14Z1760pdKWqiopd080hb9kLkxB82R2bdHphWNbd5xkPmKeC+YF2mXdtk9K1n00z0y\naW2n+Km06BJKNnjbkIW7775bknTzzTdL2ra+I9ePf/zjd+7LZ7xsPiasC+Ykyz34+f47dCR/kRGX\nZdrddFLm67XCv/sAngD3SqVn9iC/l3o+KWhFS/N+DRxDtlxGGKNLLrlk639pl3b/z/7szzbH8Kgi\nW40avRXuzBzclh/UkPmW7uXPCKGWy4l3+Jxzztm55kEwHpnBYDAYDAaDwWBw7DAvMoPBYDAYDAaD\nweDY4UiEluGGctcoLspGEZi0cR4WgsuXkJNG1Yp7393DuL0ITfPf4X5MGmZpcfnhuicsQVpchZAD\nuAuX9uECdCrTu+66S9JScdldf/QVd25zY+KSa6FXjSZ1LZF/jVZ1n0ia6XasVb6mvSS3Ohr9X/av\nhbJxfqOyJAzE3a7MF3PlcpY0vJ6gSRsy1MiPIXsug3zmmh76iLvZwySyf5ms6p9b5WPu0wgEMnzz\nMEE4lreF8AZC/JyQgfVD/973vvdtjrHeSNb3MDBCLzK5tR3zEDHCh7i2h5lCg8uc+u/QAZzf9Blj\n3Sgvk67dj9EvH7Ncy+7CzyR/D517+MMfLmnRhy2ht4VcrIUk3Fs0UhA+M2+eSJzJusCTTAkze9vb\n3iZpu+L9c5/7XEkL2cfrX//6zTHuQ1grIWbSQuBCYncLXWYdNZrxtWT11GG+z3Bf9ICTUxAyRwid\nk0UkqQCy5e3OJP8WZrcWbnPUwJgSYtfC0vnOdXOGuvsxQBiYjxH7CWHlvp6g4EdvoL95dnG0EC/I\ng9Az3ibWBfqmhQs2Eo1Eo67nPqwzn/c1yuK1+9xbkHrg+yLtQ6+2kOv2PJThTo0koMl69q+V1OB3\nvocii7fffvtWe6VlviGM4hxpWfetLRny7v1sZEIA+Wx05UlL3kKggYf6oj95XvN9se0vJ8J4ZAaD\nwWAwGAwGg8Gxw5HwyGTCrrSb7N/efPnOaRJ5u8Si1Cji0mvj18Ki4FatfAPlbde/4z4ve9nLNsf+\n5E/+RNJihfP7kZB5xRVX7Bz7zd/8TUnSG9/4RknSU5/61M2xfEttxRYbpXBaRf3/Ncs8WPPW7BPe\nzrQS+TG3XkpL4TppSdDFYuqWhDUSg7QcuTUEy0EbFyyyWBncOpGFwhpVZ/NG0Vfm3y0ttAVLjssS\n16QNB6XPziKSPj5rlpJ9ygSJgE5xfumll0parIvulciCcRQslBZvDXPcPLd4XdyamZS+bu3PxHz3\nsqa1F0ur34dru3eIex+EYtPlLK1ujcq7WV0z8dl1HYniXGuNpt2P7dOby7j6+KR31b0QEBYkqYuP\nHQmyyNQdd9yxOcacfuu3fqukhapZWrwejL0TuDDfEI+4nJ6IBt1lMovfNS9X8yrTX9rk48R3V111\nlaSFsEZaPAKZyN4olpunuyX5g31T+Dd5O0jiMO0iusI9sezhuc84kCG/F88R6AmfGzwFPI+4nKIT\nzjrrLEmLh8U9zlm412UKamXWdvOU48nxqIA8r0VuIIvtuS3PXVv7jQJ7H4B85Xd/93c330GH7snl\nINdRo1+m72uJ/U3nZvSEnwfxiM8x+hdd4oV7k5DBCUu4D/LXqMjX2t7IHpD99uyS5EVtb2iFUF/9\n6ldLWkgpWrTMQTAemcFgMBgMBoPBYHDscCQ8Mrw9uiWZN8PmXUirtr9ttsJxec32tsl9eOv032Ol\noA0e44fVBmo/t2BmUSusXN5OrCIeh431h3wbp9MkVnYtd4V+tWNr3pZGsdli0E8FWvxmWnv9GJYA\n4o7d8sh3WDyatXhtHNuYZbFS9xLgiTnttNMkLfMpLfKFTPm1kaG01PnvsMi4tTALP/r9sOg3qxlj\n1KyM6Y3ytUK7WqzzPq3v6IfHPOYxm++wqBHn71awV77ylZIWS6dbusj3oA9uIcuCig1cy+k7sdYT\nx97yppBFp7pl3hljt5Bi9Wq08Jm/4H3nWi0nJAvTtfwSzrngggs2x/I+bR2teQr2Ae7n1sGMVfe1\nkjTYzGPzbFP4k3wYaZeS+ZnPfObm2C/90i9J2o2/lxYZ9LkFrE3Gt63VzNNs+njNqo1uaDTVWKzd\noo8OyiK9jUa57RtHISemFStsuRjpTURefI2S/5ZFp9s1fW4YU+bPdQrjhrfGqfzTk5pFwP3a6JuW\n69LWB2sgC+p6O9v+m33hr+8fKQuur1I3nKri2uzDv/Ebv7FzrHlkUlbWil461vJ605PaokLyOtKy\nR+OZa88E6DD31qRsuE7JKJQWnUM//X7IYstbTy+WX5N7I+eeq8pnSiO4fJ+MfIxHZjAYDAaDwWAw\nGBw7zIvMYDAYDAaDwWAwOHY4EqFlYC3EwV1xa0mGSTvqbjrcuo0yF7QQibyWJ+URKkJokbv3uB8h\nJ55IR0gZrkMPX6B9uPKgAZUWV2GrSJsVnlu13LWKtM2V1xJIwT5dwy1cIUPm3E3PuJAU+ZrXvGZz\njGRd3LNrYXJrVepbeA4Jwb/wC7+wOUaIwNd+7ddK2qaCJoyD8ESSMv2aSQUuLXPKdx6iwuek5pZ2\nkxI9xAC5JISikRIAT0CnP9zP27kWjnVv8ahHPUrSksAsLXNJ8qYnXz/rWc+StPT5tttu2xyDkIH5\naDpkrXI9ffdwUeYSOXHCkKx43QgE0AkuZ0lQ0So0g+bypw0tDJD16zIBkJuD0ovuk2J5DUm+IO0S\nsLjsEjaUoR4uwxlC4dTlELigUyCbkBZylte+9rU712SMWWu+7pPatyVKJ/mN66kMZWmUt2sJvhxz\ncgpkK0kjWmjZGn3/2rF9oxHFtP09w2GQKa+wfuGFF0pa5tT7Q4hPG6MkSSDEz7EWjoMMcl+XqQzt\n82eCDEFsz1ac7+FDrBXXXfm7DBV1PYTeQG95SBPjmuGKed5hgzFnj5CWcF767s9mSVThY0cfaHsj\nSlkbc/rpOpe9ADnydUjbk2jB24C+8vEkNJRnA/73NrQQuPzO79cItECWlWjP6xCevPzlL98cIwz+\ngQ984E4fTkYmxiMzGAwGg8FgMBgMjh2OhEeGt2K3AqR1yq0VWZjHrRT8jnP8mljB1goBNcsRb6UU\nVPLE7rvvvlvSkrT0FV/xFZtjvGXyOycJoABbUsRKi0WftrilhWKZD37wgyVtW3/ybdiTf1thQ5B0\nxq2QJvDx2ScBQLPkrXmHGD8vbAcoTPeMZzxj55rpxWp0k0md6N+RUI6l1tv3qle9StL2mGP5gRTC\niRygIIT+1QtcYVFBFpAtaZEX5MytL0kX6VYOPBtYqd0yl1Y3T3zFypMWIe/7PvD1X//1kraTS+kD\n4+8WJMYBr6kXk8sihG5ByuJ/Pu+MEX12z8p1110nadE57q3Bssa13KvE3KCfWrK/zw1gLhtdb64f\nLzSW1nm3gqFDkr7T0dbmmsX9VBTEdKRn02UyqbX5vxEz8J2vUXQ4MuXz+O3f/u2SlvF0whHmERpV\nvyZ7xpqlO/vUvKeNCIDvkJFGCdvul8n9jRRlzZOfOJVkMWt7XENGfdAv9nZpicJg3pqnkt/5fo2+\nZ2yd4pq9nGeAiy++eHOMMcVzcP/731/Stm5PcohG0pOEINJuMW1vL/LRnodaBExeO70YjQCCsWvy\nvQ9wPzz00qKr2fv8+WsNSULV5HrN08HvvFgy12zFrtMT697lfC5xXZTJ+n6/vE/z7ua6b2jUzNkn\naZnnP/7jP5a0TWf/Ez/xE5IWWnp/PjkZmRiPzGAwGAwGg8FgMDh2OBIeGayaLf4WS1YrDsYbpVtF\nMwZ1je7SPTlpbfC28FZLwTMKZvn1W8Ep2tdyOs4//3xJixX9oQ996ObYwx72MEmL5+ejH/3o5hiW\nQOIooRb0dtJ2twhnwaJmgViLZ14rHLkPZI6UtIx1a3tap70PUKaSh+RWLWJ5sXy4vGQ8daNMRCbc\ncsE1+ev3o13kmbjVDwpuvEou88Tiky917bXXbo7hnSOWv1nmmucBWeCYW2uTtpOcEj/GPLjVvnkO\nDgvpDZF2cx5cFzCO6AQvUMgxt2IB9Avz5jKf1jafB9oFNa+vdyyrWMa8D8huo0NNj4G3JeOt0SXS\nMjdQCPt6wALLfVxeuCbeoWatbblDGTPeihvvA4xLo0hNL4a0jF/qErcOM55NH6KnKa7quY14UvHM\nvOlNb9ocQ4ejt30Mc/9r1LXpNffxzSLOazlMPhbpZWk5gJn30TzWByl+6FjzjhwG1qjAga+j1O+Z\n/ygtehqPXCsHwTg0z/0f/dEfSZJe97rXbY5lDoB79Vl35M3hUT333HM350BDjzXb5aXpe0Dbaac/\nu2RES3smS0+O53tw7eYdTPlssrQPMAbulWYe0Zl+LPfD5nFqSB29ttf6NTPX0cclix43emLu10oI\nNEpo5Jp9seXUtWLJ6a1bo3Ru5RrIOePZV1qKeLY1O/TLg8FgMBgMBoPB4BMa8yIzGAwGg8FgMBgM\njh2ORGhZC0fBpdaqbGeyv7tG053sx3DhNQq8TKryatCEBRAe4kmcWQXYj5HESXVsryKLaxNax5ZE\nj7uTkCO/Hy5n7y9jhAvXQ2loF+EyhD1Ji2uRcVlLoD3VoWU+Luedd56kJbzD24IMJeW1tCTWIwue\n9I2blXGFDlBaxp9rekVaku5oi7eTRHzG2MPHmHdCBFwGaQtuWXc50y6STa+55prNMWhfaYsTTuA+\nJvzEw4+QExJJnUAA+br11lslbYeWIbOXXHKJpO3wIyfCOGycKNlU6q53dAghpB7iR2gfFbv9GGD8\nPdyQe5OY6+Ny4403Slrm+LGPfezmGPLCHPu8o19Yr96W1FkOvkP2PSSBOUXWG62lh7ABZI8+HDRc\niGs2PbHPsBGSsH3dZliMjx3yj+y3sGbWLeEf3hd0wmWXXSap6wTCFj0MiCTvpO+WFllgrGlbq7jd\nErsztKTp6JZ8nWvGx+BEoWUnO5cttOlUUXU3opgWipjhME3mmWf2IA9vZk5bWM0f/uEfSlpCy/x5\nJO/37ne/e3Msw3fRM04WcPXVV0taQtGf8pSnbI7xzNHC2jMU0PtLXw6S5I3eb2Gd/nwBGs10/m4f\naOuBNQYl8BrVr89njssa+ZGPOXsIY+b6OH/nqQQ8/7KHeeoEx9hT/Dqc354l0Hmc7ykWGSLc+t6e\nzUDuA9LuvvaDP/iDm2N81/TayWA8MoPBYDAYDAaDweDY4Uh4ZLCGtwTIVrSJt0Q8JE65i8WD893y\niZUyk/Ok5e2W83lT9/tgZXAqWjwdvNW6NZzijLT3pptu2hyjr7yRYhmWFpo6kviwgEtLcinj4mMG\nCQHWZqzq0vImv1YEdI1OsyXxngpaVSczIIkay5V7v/B2MX+Pf/zjN8eQL6xjP/IjP7I5Rp9J+vYk\nPGQAmYIe2e/9pCc9SZL05je/eXMMCwl0wSRc+/Uz+Vva9ohI28VVaQPWGrewYJHjr88764BxpACk\ntDun7jliPGmfyzWFKSk+6R4LJ604bCShg39mHptljTG4/PLLN9+97GUvk9SpmZEh5srlnO/w+Lnu\nwWqL1d/XLdZ6jrmlC9l79atfLWl7jqAOz2RRaZln5NTnKIv+uoWU+WokFugV9FEjuGiJoElhe6oK\nIOIBdAs5fWWs3FLJ3CI3rCvfJ5ChljxPv/AAeWHLpL93TyU6IAvcefuS+tplOS2Wa2Pv4Dvu4Zbg\nTMy+p9TKoO0Ha8UB941WDHvtvCzY6PMHqQs60ol0GCPkxQlE0AHsQV6wl0Rn9L5b27kP6xAdhtfX\nz3n9618vadEVkvSEJzxBkvTkJz95p01Jv+wykbLn45bPCYyP70WpU9aKbDtOhb7wdcxzF/rYnyWY\nB/rucnCQYsmgEUqw77vXBX2MHvffJVGKy10W0vT1j37imbcRACGTfiyL8zZyGXRn618riMkz66Mf\n/WhJ255qH/d7g/HIDAaDwWAwGAwGg2OHI+GRwbK4Fr/rVt8sWIa1RNr1dPjvsNRhnWiUzrylNvo4\n2uIWU2K0scZ4waLf//3f32qnv03zVkvMK8WxpOXNFY8Mb/HS8lbMfd0izNst57uVEksL3iS33jIO\nzEOjBsQq4X1oxfIOC4z1WWedtfkOizc5IW6toO9YudyaecMNN0iSvvqrv1rStscCiy4Uxp7jgdWd\nOXULBFTHeL88VwJLyTve8Q5JC02mtIwnuS7elszbaMWh8JB4DhCyzry75Yn5avkXXJ8cLLcSYe1j\nzeBdlJZ5gC7UrVMu/4eNFt+eRTkbzTBr2fueFLfehyxw1+gwOceveeWVV0parKZ4WKQln4h5d68S\n6wjrrccsIxPIt9P98h36zC26zO0a7Tby7HKGvPDXdeSa1+sgFu99gPv6uoUGucXdp7wwBm519fxI\naVuPrhWK4xh7js8HHr9GWYu+yDwYH2fm4SCW60Y33XJcMudsrRhwQ57T7tton/eNky0VkNZkZMTn\nnWgHqPzdqoz1m+/c+8F+9OxnP1vSdo4LOhw5aXkMrEPa8rSnPW1zDjJ0/fXXS5J+/dd/fXMMjzMe\nIO4vLeujed2Qzyyo6ednHo3PbRbSbPLWClufCo+M60DGnrWKZ96PpVdb2vVmpRdGWp6fXMfzO3QL\nz2/Ssm/jDXH9nwV8/TkjKdldp1BMm754fjYewCwP4tdqETj0oXnrMl/Tn1nxNOEl9NzMw8qNGo/M\nYDAYDAaDwWAwOHaYF5nBYDAYDAaDwWBw7HAkQstwt3kYGC413K5Or4nbKymTpcUFjIvUQ6+STrMl\nceIqdJpEXH24Gp3CFpdk0uRJS4I1lJ0espWUlyRqS0sVd5LbvZ2ZTOV9py2EK3mCLyE/73rXuyQt\noVTSkryd1KvS4ppkPH1c1igL7y2yorm3YS05FRe8J9EznvzOq7H7OOQ1aQP99FCDTOiFjlWS3vKW\nt0haxtHd5lT7JgmP0AO/JvPnoXsZ1uP9I9SRkAYPtSQZuSUpZriHh+rdeeedW995uAThTq168z6r\nuHO/FsKyRr9MWI+vB8KyGJ+1BFQfM9YD9/G2cC3Wk4eBOYW6tIQmSkt4GuF8Hq5AyClj7qFe9I81\n6e1M+nqXwdQh/jtCTtFVfj/QwkbWEs5PRViRh1UQfkFfWvhCEr743sOazqR/P8b4tqrYXMv1BdfI\nfUbaDf9LAgtpN0zK/6cNa0QuLbSM79ZCy5LgoYVrtb2oURHn+fvCGs1vC6dinvO5olUZh2iGKuXS\nEqqDDHqIbhI5+PMIzxHIlD87EH6DLPB7vy+6gbXqewIglM1DfSChgUra+5kEAE2+c+34fGZoWdsP\nThURSN7P28lzJX9dP/PcluvRr8WYu+5MHejrifug610OknzFxwyZQLY8LIvftdIK7Nfsef78zPlc\n28Nq85m6EY7QP98buCbjcdttt22OMS7seWsEMo6TCTsbj8xgMBgMBoPBYDA4djgSHhmsBv5Gydsm\nlixPTn/4wx8uaXmj9Dc3t4JJ29aG9Jq4hySv5b8j+Zpr+xsl3/Gm7ffHQksf/E2bt3zoaj15m8+8\nhTtFHdfkmFthsOjytu/UoHgCGIMv+ZIv2RyDGhRLkr+Fc28swm65zKTYwwTWDLdqQ4JAoUG3wuLR\nIqnd3/CxBJCc7v0j8ZH5diIHrFhYM9yKimUOQgasXNLi/cLb4wnhyBLj2ZIMabsTAQC8PBSqlJa1\nwby7lQi5xmp/8803b47Rr2aZv+OOO7Z+57LLeLJWPMHfSQEOG8xHo0Omn24Vpg8kgvsx+k7bXRek\nRdc9OXxudOZZvNLlhXljzBrhBFYtX2NcHwubW+RY+yRYukUX/UK/XEdmAnbzcDULfCaHt9817DPZ\nv1l8GX+88+5hPtEcueyTiNss5enhdD3TEmQB12/0yZm0yzX9OukFc3nN8fX/k2K5JW8fBM1ymkn1\njY61ydi+PTLA+5pr2tdmjn+zBDPe7Ieuf9nDuY5TuSM7jToe4gBIgfDSS4u3BV3APLpHxvfGRHrg\n3EvPvKPLiBiRlr2Kdjf6Zb5rVvRcj01/cM6pKuXQ2sLa5tmH0gzSsidnNIq02+dWdLWNC+PK/Df6\n9CYj7E98t1Z6wL086U3250ueUdNbK+0WhG+F6BvFMnsze5mTWjz1qU+VtOyZTT+ueXoPgvHIDAaD\nwWAwGAwGg2OHI+GRIT6xWdewQDo9MW+pxHj6myhvzy22m2tl0R+/N793q2jGi7biUlhYnVaPz1ho\n3KvkRRITFELE4+DxjbxF0xe/H1YmLJBeIIv+YFF0KyU0sVyzUQpmwVCpewwOC8wRHhZpGX+8Ju79\ngmYSK5dbG7FYIyfebqgBocV1DxfWDOTErVppIXPv1xOf+ERJvXganjDiqr0IJVYM5MXHGotHyxnj\n3vTF+44liJwXvx9gPLzoLLlUWOvcasdY431x754XfDtspLdVWtrMOnB6SuivW6wz4/jOd75T0rYu\nyAJ1bv2mDZlv59dnzL2oLmsrPTN+zYynl5Z1Sz/da91yOBItj7BZ1EDmOzQLfMuR4Ri6o3kc9gFk\n3/U148Ic4f3087FGN+syv2fO3OOIHuT3LYcAOfD9LIvrtbyCLCLYvBi5T/l3/F2jUb63RS9bHgzt\nbB6Z5qXytu8DzZuYFv+2Hlw/5G8YK3Ty1VdfvTnGcwj06a6DknbbdSzefOjsvfjz93zP90ha9PCv\n/uqvStqmGUeWWz4KfUkadmnReehqz9Ncyznk2Fr+XHprm6W9eV9Odd4M48JzmHtdidxAVzdvfdOP\nzcOR91vLCWnrnucRPIE+5rQZOfD9MUsIOHJu/Zqs1/wrLeuEvx4phBcLmfJi3shs2zPX2nkyMjEe\nmcFgMBgMBoPBYHDsMC8yg8FgMBgMBoPB4NjhSISW4Xb1kK2kzHP3PiEbuKU8tIKQpJZUiesN97CH\ncBAOwnce1oMLr9HVZVLdRz7ykc0xkvFor7uFSUTNcDdpSR4kEd3du1lVFTexn4dbz9tC+wih8lAM\nwpY8kRik+9hd4/ukX25UhIQgEj7mSdhJh+x9odI67XX6ZdzHuPlb+Bjy5Un7Gebkv2NsCWnxMDD6\nRSiUX8eTRKVtamhczIRYejgXfSZEzMOPkM8MTZOWMDPGxX+HzBL24MmCgPlwt3CjAD0s0Ca/B+QF\n0D16iF+G1XgfCIFifDxEk7XJvHuoQCZPe9hBrh/WmrSMLWPuYSy5jlz38Jnz3d2OzmhJyhlm5Ot2\njaRjLSRkDamvT1Uld+7roY+sA3SkH0OfMS6+phOMgc8VcsIab2Ekawn5Lawqw7BAo2FF5/mel5XW\nW0L/vSVcWEvob3THGVLm/fa9bp9oYT8tlAWZyJBAnw90DjqZKuWSdNZZZ0nqY8S6y/IAfp9f+7Vf\nkyRdcMEFm2PIJX34ju/4DknSd37nd27OgXCAkGkf/0wcd52CDBMW7aUDAGPispTUzE1XJGV9o7Bu\n+upUEEA0nYaO9vF51ateJWl5JvB16GHi0nqyfyvvkaF7eZ60rTd4JvZn4/wd558s1XnSvkuL3PDX\n9w3GiL9OPJHpId6HV7ziFVv3/dEf/dGda7Z1Ocn+g8FgMBgMBoPB4BMaR8Ij04p1pdUwE/Gkxbrx\nx3/8x5vvsDhfddVVkrYtV5mg6ZZQPD68GbrVKC1m7U0ba7onymPFwRoFHay0WJI536kXKeBIe90r\nQoIVngRPruYt2Cl9AVYbxqyNZ7OmJu2fH9un1bUVLeVtHyuBz59bAPxcabFmYjH34mDXX3+9JOlx\nj3ucpG2LAJbcLC7o9+Y+TjiBlYH5c5pM5g05dQtPeh/dQ4IskRDqhAUcY6zcA4T8N+9ZJmZ7ESss\nzoydWxLTcumytM9E3re+9a2SFi+Rt4Exb4nvjLVbeJAF5gZabGkZdzxqTsnNuLSkdrxBfOfWLCy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2fGB/3huoWxJhzY933GMXWLH2OuvU2ZEuC6IfWxXzNl0mU6++xznKFzbf5bCCaf0Rfed8aR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\nKLIZF8bDE+wf9rCHSVrc7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/JkCp8qOF2y7IDTP+nHOvKEHIwR+p133nmba8wXrqQuZ3gv\nLnptTh/ykIdI2k6awvqhL7hQeqIOvmfomycEeNzjHidpcR/ytp0/pO15h+eR/95fvokyON3XDr/R\nzvXXX3/K530N7fNborkoZSiBy4jm2ptogeutrbzWQhjym875h7/Zp9r3F8+3FNLNbZxEMN/93d8t\nSXrqU5+6uYZLP3PF+pcWPmFfbcmWkI9tX4UnPOFIurC1ZAYHwVhkBoPBYDAYDAaDwYnDsbDIcPr3\n4NhMg+wnWE6CLT0xp8YWmJ/WiJbmDg2fnxoJpiPFqBcHoy9oQ7zgFZpPtOJ+uiUwD02VW4BID53F\n8LzPTUucqf28SGMWLfPTNLRtxYzS+rFWuHPfyMQKzVqzFujZUh+iXWZ8Pna0GdDMNTVpFWxa54No\nol0bjqYKTaDzWRY0dW3FBz/4QUkL77qV50/+5E8kLTzvgbauSUswLnjfNfOk6yTw1K/tU/veCpqi\n5WF8nhISGjHfrmFlvaYs8d8a0rK5lkqypTjPoEhp12rSAvrbumX+WIc+71mY1Od6LYU778Fa49pF\n+Auaez9ddmdf9hnc3aysaRFrQfuZ3KOl30YWtDSxLRg2ZbPPwZrczsKS0M55i/2oWYnSItISAbQA\nXcacVippmWssApn4xPuQ8tXboh23Zh9G0/rJoPFEjtX3McC1yy+/XJL0ute9bnONwqAPf/jDJW2n\nqsZyTjIf/wagECJy6corr9zpJwlf3FqWngV8Ozj94Rf43It0IqO//uu/XpL0mte8ZnMNSxrysGnP\n4Ru38uY3QEt0ghxIeSDtppd2ftvntwR7w1qgfCt+CtYSHDWPmCZvcr/w92UiJZdF0LglbUkriPc7\nEzJ4n5hHZIIn6MGCyvPOy6AVZ05rbvv+gqedTxlfG/thviXGIjMYDAaDwWAwGAxOHI6FRQZtjZ8a\n8RHlFOfFK4kBaH63mTrVT3ipofNCUPirchJ1f1NOvviZUkhIWjRkr3jFKyQt2nFJetrTniZJeuhD\nHyppW+NCylS02p46kfbxq3WtCKfhVgQJS07eIy2nZ675c9AYrVvTjqBhcYvFPuMhKPLo8RdYxEgR\n2N6/poUFbllJS0xLzdysIFlozJ/LmKODpHOUljlBC+IaK7cmSNvaMKx56fssLVY5rrlmlnlvxUPR\nmvCvv491h5bY37sWc3JXsRYbh7zworMXXXSRpKUAn/cNHmhpQlN75trbLJbZ0nw30H5akPy3FtPB\n34y9xTG0+JnUNLbCq60AH2C+XZOOdQ+5RIpWHx+08uKcLruPGqwVXx/IALdyg1NZ81ua/oxdkXZp\n7byRKbKbpwD84jIo43SQRd52FiZ28L7mhQCfNY0n97WYLvoCLZp3QFqempYZtKJ7+4bLCWgJz7c4\nBtbhS17yEknb/POMZzxj695rrrlm8zf7UbbjfYBf3FIBbZGj7gWScZ35u7fJb75Xsn8yx+eff/7m\nmn9rSEs8nI8FWeTfM6997WslLXvImuUafm1xG2ueEfvEWrkNp2uzfoNMv9/WY7P2pSV/zQPALbnQ\nJS3z0m5sTPNa4t9mneR7z78JaLMV9UxLavPqaSmouZYlVaTd71Gfh7X9KTEWmcFgMBgMBoPBYHDi\nMAeZwWAwGAwGg8FgcOJwLFzLcF/xgFRcBTC3+jVSEN55552SujsLJj93f8BdjPe5Ke7DH/7w1nNu\nJsc8jxnMUxm+9a1vlbS4g7lZkFS3uPd4AGwGb3s/cd3hfR5wRV+a+0SaST3Yfy2NclZ4dreXNO+1\ntM37wK/+6q9K2jZDQtvv+Z7v2bnGuPjXXSigY6sMn4kDmik8U3dKy/zRJ6cnPAT914Lqm/sJ7lHu\nJpUJB5zncQOAr31eGCvvcV7CHasFBBPACj3c7QR+YW16X/bpWpbv9/7xL+tRWlw6CbR1l49031lL\nDuFzmwHy/hz3wyfuYoBLCe6RniCBZCDA3XfSLdHlC3xF/9xlBLnQ3C8Ze1aE9zEwTg8EhfcICMWV\nVdoNBPZ11NzhjgrMlcsCxucpoEG6aLR09GtpVJOeLRFAS0pB+xmEKy30T1dUvwfZDC19ztKdw+nd\neDjbzLTh3j7z2miRbk9tT2nJUPbtRrSW3KGVLcg9w13AAe6p7PPuOolrFjzo+wuuWchK/wbIhBGt\ndEPuT41veN5TJUPjd77znZK6Kyvp813+sGbYC3wNpdtRK2OR8sppke5Yzi+HcSM6LJqrfPazud6m\nO6j/1trJ74Xmit5c2ZCVyOw3v/nNm2uEPzz72c/eutf7yXvcXRWXsOY2nu5xvh4ZX5Mb6ZbbZEpL\n5MGeAC95P5Er8ITTZc1VOzEWmcFgMBgMBoPBYHDicCwsMpwsXSPMSQ2tgwfJYa3hWqYalRYttWsi\n0I5w2vTgOk6NnGA9yInkArTlaZQpjMW/fmJG+0qwq2u1eTenY9emEHCHZsjT1UEj+kLgrbRoaGnb\ntQepdXPNIiflLAQn7aZadE3yPotYPetZz5K0TbNMS4wVzdEsTqmBcDCGlp40A3qdlzLNpGvxeA98\n6Vq01AT5e9HIYDWBf7xN50uQxQjd6sK7U9PubbagaCwya1rploZxnwkgGINr2KAfY0dzKkmvfvWr\nJUnf8A3fsNO3TNHaeKMFdKbW2+czEyq4zGJNNp7NAny+rjKhgmu8kHFZBFiSbr31VknLWnFNMP1j\n3tfShbqlg0JqvNe1tcg4xuxrZZ88AX86z7NumiUI2iYvtTTaLVU2aAHLqTX3+chkMk5XNJRJp2Yl\nbMHCzAeysmmQm6Y0rS1tDcAbPO9rj/HxDt+HM+C5WQv2haaBz7H5PZksAUvu7/3e723uSS39jTfe\nuLkG/aF7K0LLOvZ5Syu7WzizcDL3OO2SX1zTzTdDk/HwLHzqa4dU0HiIuDcBvJuplQ9aeDStkS05\nzT7giZsSbX9DViKXXY4wH81ikZbYFpifKcu9TZ674IILNteYR2R2s7ZeccUVkrbn+OlPf7qk/m3d\n+BOsJdQ5SIHoVpYgEwc5n2YymrVCv2sYi8xgMBgMBoPBYDA4cTgWFhlO/U0rzonZNTzEHuC/fccd\nd2yurRWX47SZaQD9PvxGPZUh1hZiV1wTwn1onCicJe36zHrMSp7sPaUzf0MX1+LSh0suuUTStkaQ\nEy8+u55eGs1s0ySmRso1Llkwsvl97gOXXnrpKa/RX9fo5JyupfFrfq1Nc8l98KXHrKC5QhPd4gVa\ngTrQLEFoNNF0NyskY3atBmsDvnRtb2pYfB3RVksN2uJtADRDs9asPPvAYx/7WEnbVjr6QsE352ss\nsKwfj/fgvpaKeC2VZFpiXIZAWzRjTs+MR/I54jf4y7XXOUcO+gwPuszifS32i/lqsYLwFVpa7wvj\n4n3Ou/QT8NMm2QAAIABJREFU3l/THB8lWGOudb3pppskLbE8TcuXMXXNipHxV/434/Xncq/CqunI\nAsM+hnyH05ffoKvPC+9hXbiVPjWkTfY15NgZr1uZkFdrqd15h19zPt0n1goZNq0y8/aUpzxF0rYn\nBHC6g1tuuWXrff4NksUKXV7kNdeoM6esR6xETdZmDK+08AL0d5mZnhfeJ2QW84UV1tuCLsiIFjva\nrBKA+10u7NM6w5y10gpNvjIGSmM4fTKNebPInOr//ryvPfrVis4S34l8c48Y5or7/ZuAvSQte96H\nVgx6re8pE9bKSjg9k5d9HjI99CdbLHcsMoPBYDAYDAaDweDEYQ4yg8FgMBgMBoPB4MThWLiWrQUP\ngWbGIm0gbj7Srrm8pQFtgVqY6Qjc86BqTLZc8+dwX8FE2SpaYxb0MWBuw8zuJtwMDHVTPGZd7nH3\nMYCp0d1lMANj8vfgX/7G5Mc4vS3ccz5VrmXNxMhvzWyd6U29bx5s7/d4G1kl169hmnazLnOCS5mb\nS+EX+MxdFLgP87W7euFaAJ84D/IbLgL+vnSFcfcD2mf+fQzwJfRovIRJugUe0wd/32FSJh4Wj370\noyVJ9773vTe/sf5e9apXSdo2r9NPXDW9b9yH+2YL6F9DSzOMOwa0dldE3Dhwd3I3N+h+9dVXS9p2\nX8EdlefcJe22226TtMi/BzzgATvjI8jY3U6YU9aFX4MX6IPTM92MWuAy97i7r4/1qEFfPNAcmuFe\n5+5Q6XLTqninG5C3nen5W/pl5t2TIUDH5sKa7le83/vN+kM2Ox/gDk1ZAF+PWRl8LeGJy6JMfpOp\nxf1vrvnzjIWxeX+9kvw+0b4Z2rdGpuJmP/z6r//6zT2kxGWt+J588cUXS5Le9773Sdp2c4Te0Nj3\nJb45Wkpe6MUabe7wWZm9pYlHvrUU6+w3Lvd5D6ncvU+4WrHntRTNjIlx+7VMKe3r6pN1KToIkI/N\n1bDNC7/h1uWlMfLbsSX7aKn5c19saY2B9yW/sTw8ge+D5kJLP5l/d1dLN9Pmbtq+hw4SmgHcrRo+\nQQb6PPB3rhN/30EwFpnBYDAYDAaDwWBw4nAsLDItcBJNAKe5FhyLdsoLOqGl5NTo6QM5QbYgNVIe\nZ4C3tKtJ8NM1J2ROoP6+DJzz0ybaCNLFnn322ZtraDDRhrlml/fRJ9farRXIQhPZguEzJbMHqXJi\nhtauad1n0OZaIBlwLRNjTe2atGgK0VK1gH7ucc1Caq5c0wn90DiR7lZatOBoTLxNnqPvnmKZ5844\n44ytvkkL3eEv16ym1aVpkFkPzvOMi3993tGetBTEoKVt/VSkX3aN5w033HDK+xkXzzUZ0gr2JZr2\nrAXRMw8t/TJrjPXrVkL6xdyiYZcWvoZPfN2S8hv551ZWrDPvfve7JW1b4jI9sAcCQ1vG7BZm+tKC\ni+k71keXPVim9wHm1jWOnnhD6kVuM9mKywT4mWvO02mh9OdYd9DQNd3IT9p0mkOz1My6hYO1tlZQ\nkwLRbkWDR/itWdF5b0ufzBpKTa33hXtdQ5taW9dq+zrcB1oK2Cw+6UgLP33GAiEtRbhZmx6Yf+21\n126153RMy5/LEhK4sD58TtlX4D1o7esYnoc3nP4pg7zUBHA+AcgQZIMnGoLP6HcmtXHQX/8eYiyt\nkGam6z5KsL6aRYb58Wvso6TYdt5N+d++JZqlIwPlW+KBZuWhfebD91rmoyVPgO5ZvPQTvS+9lpr1\nZM2DimvtGzLf7/1rmIKYg8FgMBgMBoPB4H81joVFpqWNzWJmrm0EnObwZZQW3040gq41RNONNoVU\nxv4b/oZ+GkTzkan3/L6m9aUPaEzcgsGpFH9hj/PBAkMaTT/RUpwTnHXWWZu/Mw2vF7qif2iEXYOZ\nmnmnNW2uFVHaB1rq29Saez/RfjIfrhXLooc+ltTeNe1t8zNlbl/3utdJkt773vdurhGTAb+5Roc+\noH1z7UZq612rgXWn+bintta1ptABzZxrynk32jqn2ZpFhr+ho9PzIJa0Txb08+abb978RjGwlh6c\nv4k/8zGwxtCMt2KZTSsFf2RhTGmhf1o1pGX+kA8e/5Sxae4HjWY117YkPfjBD5a0yBW3VGGlYU0T\nSyTt+ub7+KAL8++8y3Mt5i8L77kmtml+jwrMlVuKmdOWMhispSBNzbGvuUyp7rTjPVzzcSMTslCh\ntGtdbzEyWB6hK+l4pYX3aZtYGR9DlgLw97SUsGmBQTa45jyvtSKTvB8tvvT/Jv3yWqG/1PxCT98L\nzj33XEnSVVddJUm67rrrNtfWCkPDi1ngW1roQGmE3Nu9bdacyxvWGv11C/Dtt98uafmucAsgf2Pd\n8XTdqcF3GtIHxtD2sIyt8Gu517os22fa/hYLmL+5ZSDTYrtcZR1lgUtpVyY062fz6kn+a3tRpil2\ntBi3ZgFMNCvrWgxQWs1aXBMy1y2W2edmHWpxYofBWGQGg8FgMBgMBoPBicMcZAaDwWAwGAwGg8GJ\nw7FwLcNc11KZZtC/tJi4MG25K0aaTd1cm1Vc3XUHEywmR3cLIMCvueBgQqNPVPn1+zAdu/tRVvP2\noO+sqt3cUHiPp21mzLTlaWpxU4COzSzItRbU3tIx7jPVbnNRyiA1nwdoRv+aO0kzl6eZ28dEW/CU\njx03oIsuukjStnsjbTAfJHSQFhrzrwdhMi7cG90955xzzpG08GdLg5pjkhZ3A/51ky88i1uem4DT\nHa+ZtKFZq26+D1CxvVXAhnbuPsCYWWOeBjirrhMULe2m0XSTeroNuOme9crz7vpIv+AlpydtNJ5l\n7WdSCmmRY7Td5o/Ad3ctgX7wks9ZpvR0uqSrhK8j2uJ550mXbUcN+uR0wa0OmeUuNxnY3QJYM6De\n3Xl4Hvo21xlo7UloMgjW3ZrZa3BdRc44vTNA3p9HFtE35213h5W210cGILeU0EnfFsi+5oIJP7gs\nY14uvPBC7RNOvybfPxFa+lz+9W8Vd5uTtnk/Xft8HRLkz3z7XpDB9rg3+fMkCIJP3QXqVPf4e+AN\n5y3GB082V0L61tyXkCVrLpvNjXuf3xLQzNcCsrm5lvE39/tz6arVXJnbeko54+/LdejvyERRLSlB\n6wtz1BJfpBx3+ZZ9yRAP719LlYzs8Wspgxz0667O/1hkBoPBYDAYDAaDwYnDsbDIcMJzK0haafyU\nyn0t1SsaSLThfk8WqnNNHZqEliq5af2Aa6ik7UBLtHYUyvI20VKilWlFDLEmuQaasaNlcC1OphR1\nKxZ94X5Pq5padB8TbaJZaykMP1WAfpkSVOqplQHzzT3OE6lBalpttDYtaJmg2wc+8IGb3+gfGjKf\nI6xk11xzzc4YeB+861qK1CC1Al7Muwf0wwOpRZOWFJ/c71o7+tLWWkshCfaZfpm1TeC0tKvdaxry\nt7/97ZK2rWZoOllrbr3M5x0ZfNksJKwVT5WahVN9baZm29tEu8v8O33hs5ZeFEArLCz+NzLEn2O9\nsy5c65cJB1xblwXYWrrnfYC++PzBs83SdarCe2uaVUcG9K4V8vP1QdIEaOEylvXHbwSBe1A8coY9\ny/mAvbIVGsy0u659z7H42skA/lY4MK2SPt605LqVwvfIfaIlNjiIBjjvlZYxft3XfZ2kbSs7dGBu\nmxxtFhn+Zk7bfsqcsmZ93lnbye/SYqVraf5zfL5fANZzS4zDfpqFtL0PvMP7m+P7VH0/QHvfv6E9\n8srXGh400NCtJ6wnLA8HtSSkpcO/6eCflpAHWsNTzpP0PRNKOGjLZTXPraWQX0u1TH9bemne0wpo\nt7TU+b3QeOkgGIvMYDAYDAaDwWAwOHE4FhaZViCLU9xB0vL5CQ/NJ+k4/STatGGAU23z8eZETj/9\nGqdbTo9oR/09rQglp1riZlrRRE7j3iZ+18TGNF/dlqo3fbpbkb8W85CpYT1NdPPJPSpkcTL/jf65\nFoW+U8TKT/rMF1qU5tvPOF2bRhsZk+XX0PK4nzSxGNCcNLnS4rOMxtrngT4w/15IcM33mL/R6LiP\nfMaJ+HNe7Mz76+Piva7tSa1Ls5buA6w11zKzbltBU9KKEgPkKXqhC+NqmmbQCpS1QmqMnbl9z3ve\ns7mGVZZCla6ZTQuqW2QyHarzPJpttLVY2CTp+uuv3+qTx2ugSeUahRSlRdaw3ltBYWjsMUf0Gd7z\nIr77LHZHP30PYb75za1fpyqE5zKv+fCD1MC2WL5WZJW1Bc1amlH2C3jS11KmgPfxcp97GICMa2jW\naNAsbDnnLiOgXYsbYezwga/ZfVtkWtrbjHFx5DyvpWqmXAL/SksMbYsFgF602coYQGOfb9YoY8Ey\n6vRPa5nLFMbZ5CJ81qznvKfxWaacZyzte6G1nTzl2GeMDPD9lO8n5IfTPmNpneasXywdTUa0PZrx\nsQ7c0s33CevXZQO0xaLmezvfunx7OP8xnmZdzkK/fi0tR2vwOU6Z28qQwIstZXWz6jY+ORXGIjMY\nDAaDwWAwGAxOHOYgMxgMBoPBYDAYDE4cjoVr2VrAZLuWQUpugsIki8nQg6ow2bWAP8xy3O/uCGmK\ncxM+Ztps29vA1cSfw0WLe9wsiEsMpkNPgYqLAG5d7tKEOZDAV3+fp4KVtk2UXGMM7k7CmDFj+3Pu\n8nbUSJcGaT0ADbM6JlUPCF9zFchA5Fadmnu8AjLJErjH06FCd0zvZ5111uYaZlWCd93EzNzCCz4P\nGVzurjvwEnPjwYKYsqGdu3XQBu9zVxP62SpSp3uOuw45rx413vzmN0vaHh/z3dyBcB/A5Qp3K38O\nunhAOOsgU/RKu+4cLfiS993rXvfaXIOe6bohLe5iuD74tUz9TSCq/4YMwbwvLbzaXCbTnc4D83Oc\nLluhEbzuPA+fIRPcPaK58h4VcPXw+aMPuDe4LOA+6LqWuKK5nTW3EZD80lyJkUW+tvkbFxPucdlO\nf7nmbWdSEnf5YI3Tb6cTfWquYazpTAneAnVbalj4hn5TaV7adWk9arTxgDW3QdCuQW942eUvgB5r\nCU98T2ZNpqxt781EMP4bfO4yGl5Kt3hpN4GL8z6ylXn3a7SPvMl3eJvNbWmtavw+A/9ZR5deeunm\nN9Yc8ti/97JPTtdMre4ykPG19ZBu477vM4/I9ubmxv3NTRdedLf/dPFy3ko51b55muxLuea8zHcF\n35JeEgU3TO7xb/L8Fv9kXQzHIjMYDAaDwWAwGAxOHI6FRaYhgyj9dJsBbK4B4SSIxtxPzBmw2FIe\nAz+hcxrmZO6FLV3bJ21rH9Ge0HcvbInVhNOqB0BxKkVT69dov1mOOMm3gGv6giai3QM9WiAqz7sV\npgUuHhXQbrTg0hZkjlYBGvu8QCs03a24aqYplpaxom1u2nC04G7xyrS2bqUgMBRaE/zv19aSQ6B9\n8fFRYI4xuLaHtvjNNaNpbXFaZ8Cxr7E1jaenOD5qMHbvC32G1p6Ig75TeO+6667bXMOiibzw9Z9F\nUh2pOWpF99C03ve+991cg3daYct8n68r5gHedQtJ8i5WPu8X8q+lQL7hhhskbRfxBc0axbtZI60g\nWtMqu/XhqIHG0dcKMgD56alyWcus0RaYD1qK5YMUvWteBPBiJm+QFh6GpxhTK7KbFkEHfIM3grSb\nSML5J9e283LK38bvuWf6/+ED5h5ek3q636MEtGn72Boy4LjxREu6gYWJNdpSFqfnhrS7jlz+Ml88\nB82adYD++txmGvVm8UgLQvYh2+Rv1hd9a/O+tkesXdsH+NbysWXx4iajmpWG+WS/d6sEspa2nHZr\n78mESv4c/cxvwrxP2t778ApIHpF2Zdea15Mjk1C5ZZXvbdr2vtBP5Jl/g3Ctlc84jHVmLDKDwWAw\nGAwGg8HgxOHYWmTQ/LRUipn61sFJktOf+4ZyeuZk7r7orsWWesEyClS6BhNNR8agSIuPPH1xv0F+\nQ5vhJ+AsbOmnVE6ujMv7mWl4HdCRuAE/7WahSL/Gc1gnmhVkH2ipoFN72rSh9N15A80K2ljnCe5v\nfrvMKXPldMn3uH/qTTfdJGnRgns/0zrkGjrXpEqdB5lbrDDSMjfwks8LmhHSUjvSEuPadDRHTauZ\n6Sldu+yFVo8avK8VnPNUwACtFPzi9CWeqKU1hWbQp2mJWnEvgMbS1wpzQ9+drvAE97ssYk6Yf7fq\nMmZ4qfUFa6DPEZpgxuxWQa7RB0+xDl9lalhpV7va1uY+4ZpqaIQ20jWXzDsFKlNu+G9NJmSsib83\nrW1O82yz0YT9AZnSZHTGavj4WqE62qK/Tgva4D0tvXzK4VZkslnooP0f/MEfSFrSj/s494WMR5J2\nx+g0gia5tp0eKRNIoy4t1qaWLhb6tzTIWdjZ35cFSFscY8ZL+fPME8+7hpy2W7FExtcsOFmknLZ9\nr+U53ufvpW2PkfhUAIu880PGd67JqCbHud/HjsX/3HPPlbQ9V6yRLDAuLd4V7LneT9ZoK0fB37mO\nvf02n/B347u8p633D37wg1tjkharVyv4mXGhblXiu6mty8NYVMciMxgMBoPBYDAYDE4c5iAzGAwG\ng8FgMBgMThyOhWsZ5sdWQRvTVqsG2lybuIb7g7ucUB2V97XKyQRsuXsWLiqY7p7ylKdsrmFCx33J\nzYm031KZZmBYq/SNaa25NoCWsIDnfeyY91qQKq5JmPzd5Q56co+bilsw3FGhBfQn2jWec7Mk84C5\n01NnYubEbO4pT+EhzLMevA0dM/BRkt7ylrdIWtINelpc6IdrmAe+4vpEpXbnQWjNb56Gk3czp27W\nxc0NM7CbtDM5hK+HFpQMcm6cd72C/FGjJfdgbWWQqbSY7FlrPj7G0HgYerKOPEBzjR8zxaav1bVg\nba7x3tandFGRdpOIuMsk/aTvPgbSPN///veXtM2DrA34xXkJ3kOW+Bo7iOvUPpDJHqRlHfGvj/2O\nO+6QJJ155pmSFj5wOZpBsGsB/S3Amjl2ujK3Lj/zOdLtN/dDZDPyyV3asoq795f7+Nffn5W2fY9t\nlbYTSQN//q1vfask6Y/+6I8kdfejfWHNJYW+tmr0oPFuzq0nfklXy7YugK/fTNPvMiHdr6CtPw+f\n8FxzSQXu8ptJRdwtmm+kTDbgvyUt2nw2dzd4r62r5hZ7VMiEC94/5t5lbrpo+ncmdM304tIiZ26+\n+WZJ0vnnn7+5luu/uVcie50f0yWtBe3TP5dz7Alt/86QBW8z56Gl3wfedtLFn8v15bzItyaJA1ri\niINgLDKDwWAwGAwGg8HgxOFYWGQ4UbpGJwOK17Q4rWgPv6F9lJZgLE6yrsHkxNxSAnLSffCDHyxp\nO7A/tWB+Es2CkX4yzWBq18BkoF4LgGqpjznto+nxIF4K5KUWyH+D5i0NL9oeP5XnCf0owXy3eW/F\nk/i7aeaZmywEKO1a0txiAY3QUnmAPUkTWlrMiy++WNLCGyQZ8LZe97rXSeppornHtT0EKbcAZMaH\n5ccDa7kf7bRrC/k7U8M6WnBxamt9HbnV6qixFpzYggUzecWa5tD5JQvfupaIdcv9LT04aHJpLeVl\nQwaOu0Y9NcAtFTR9uPXWWzfX4CXmzZMnoCFDY+h8nVY6p1muV5eD+7TOsEZcFiGfWctuZWVtsO6h\ngfMG9GgFDjOBRwuexwLoVlNkAH3ylOzIVqxF3NOSKfCcP48VtKXhBazxlha1FdIEmdTC10JaYnwO\n3vWud0nqBXX3mY5bWi9y2mRcFrxeK3YKkP/Skjjitttuk7S9ntgfMp29tGvRcNpmsHwrzoh8YpyU\ncpCkCy64QNKyPvi/vzctCNLCu29605skbacuZ3/hfa1w81oCoLXA+n1aZNbQLOTMXxZ+lpaxMw8+\nZw960IMkLbRrFrJWRDpTJDdPDOjTLDnIO+e7LHbZEl+wFrwvvI/fXHYCvFhaUU/e19Z7S0qDFRo5\njAeJdLj03GORGQwGg8FgMBgMBicOx8Iik0WYpLteLIlTp/uykkbXNet5f9M8pQbST5toTJrGNIuJ\nuVYn/eHX/JqbFh20dIyMwX20M91oawOLgGvY0SSiXXStm6dmPWocJDam+XTTd9d8QUc0CK6xzPSU\n7iuL1hNNB2kHpSUVN9oGLF7SEpvEPLimBD9Y+ufarNS+tcKW2W9p4We0Z04XtDX85nShX8x/s8Q1\nX/lM++lWLNcUHzUyPfVh0filFfzMmAEsGNKiPWvp1tNC4pqnLNLX0opzzXki+dnXJn1p/ui0gQWC\nWAV/d7Mq8Z6mSYdfmG+fd/gfDZ7P1T7T7SKzfK0krTzdJ2Nm/dLfVryu0YBrWRzU/26xVcRZcs37\nBG/kOvS9BNmDlR+rrbRYdbGG+nymVd/31fRC8L0lLTGZVldaaM473BrLXsvacevyvuOnmlUwLa++\nvzRLVCKtOx5H+rCHPUzSYpFpxW7pi7830+363MDD7OGtoCky6HGPe5ykJc2wJJ133nmSlvXhmnX2\n7d/93d+VtG2thYfhs7VYB+5pafvhLV8f0AIeanFp+0CzgtPPVrIiPRRctkDPFscG/1PYGhkjbVvL\nTtW/lnYdGdasrM1inEAW+H4D/e+8805J2981xK80mQ1fM2/+rZR98L0hLbAthgzLjF9rJSNOhbHI\nDAaDwWAwGAwGgxOHOcgMBoPBYDAYDAaDE4dj4VqGSfUoA76a+xHVeDF3ezBmVqltqWhboDUmRsx1\nLXBqLV00bgtuqsYUl9Wc/b5M0Swt7gq8jzS+0pL0AJcPD0CHRphX/Rpp8XCN8KBvru0DLSVlVkd2\nE2xWp3cXHMzB0NXN3bRJW/4++CNdf6SFRrh14FbgfQFu0ob34BenZ97f3CuZB8zX0hKgCS+6KTcD\n7dycn4kjWkBgc3tgfLiLOO+2JBRHhW/8xm+U1APJm0sof9P39hymdOelTD7iNIMnaMtN6DzXXLbS\nRcd5Kd/jtIae0NpN/pl4wIH7yMte9jJJ2+5xmdayJcZoQbAZ0N/cQVqKbPjjMK4CB8U3f/M37/Ql\n5a3zBG4iV111lSTpmmuu2Wkz3STW9iV3FcqxN9rRpyuuuGJzjb6n23BrG7nR3MDSldn/Xgtgz6r3\n3mb2cS1Q22Uuewn/ZuKbTzWYi8a7p0rX3GiW7UiL+zry12V6uug2NyD60tYMKfyR+y5fH/jAB0pa\nqqq7C9QNN9wgSXrjG98oadu1jzm5+uqrt/robaTrrbcBn9EXT1SUsqi5OTZ3p32iuYEy583lL12n\nnVey723PPPfccyUtKcilha5tf8y15TRPeez9zPTJzZWZdedublzj28X3ML4PWcs+vuRhd6929/kc\ng/OetM0TKbPcNR06HgRjkRkMBoPBYDAYDAYnDsfCInOQFMtrWDvZu4aAIOxMRex/cyJ1rUpaPzzI\niWto/fy5PLV7PzmlcvJ1bVYG1XrgbBa6c00rVhee90JXjBmNnmtROO1zaneLDO0zPjRD+e5PBTLt\nsmsLMk2wawjQgqB58lM/42mazizy5M+hMSN43xMroLVHG9KsBA2ZztQthgT0E+Tr1gXGyr+u9Vkr\nesnfTUOWgYSuRcmiV66Z2Wdq1Wc+85k7v6WG1ekCreFvt4hyH/PuGuPUcLnGEv5i3j342TVb/ry0\nq3lqRW7zX39fSx2eaZdd9hDc3yxHaCbbvKcsbUXPgPMSaNahNZ6/q0Bj7VjTVGYBPNdi5/P5zCdC\nWt28nZRZLXX9WvrubMf57pMtHpzPrwVcZ/B/gyd/yfHukwfuKtKSumbFakkT2BfYfz14Otvw9ZTr\nvWmq6VvjF4K1X/rSl0rqKW9bYD1yA0uOfwtwjfe5vElPA8bmPJH99GfoC/10ftsnf7S9Ib1jfN9i\nb89ilNJumYdWwJHESmedddbmGh4UBP372LNguvNWWmJ8HrOQsn+D8A3H90Ir4cHznkqcsScNpIV3\n+R51ejKnmdLd37cmzxo9SQBwEIxFZjAYDAaDwWAwGJw4HAuLzGFTLR/Et7JpmbCCcAJ1TQQnUE7A\nrqnjpNs0EKlNcR927kdT7s+hASCuxS0InGA5pWJJ8jZIp+jppTmFoyn1GBk0yJyY+b+0xFjwr1uA\n0FRzMndrjWsFjxr00zUQa5pDTvKtyF9aI1zDym/EuHiBSqxY3ONaBq41jTCgn64pgV/gPdd4QE/6\nwHz4+KCHa5AYD7+5T2r2vVlkmsYk/ebbNdaKv6+lUj8qtHiitHa69qxZDAD3UeTNLTKMocWhZZsu\nQ9Dyp7VVWujfYnkyHqUVYGtFB7Eiw+vOZ/STIr5OOyy19N3XE/fBi2sFfn1tpgZ53yl2sw8tvmOt\nDwe1skgHS8/raBaZtM63uKRE2+daPMVB+tmsCwcp4JrXWp/W+tnwqYqNOGgB3NSErxV8bRZV5C7f\nFbfffvvmGvyZqYf975bmm3VEDB99bN8lWFszTkFatNre36S/jxP5Qts+jy0FvPfN28oYFO8DMmat\neOZRoq31/JZrFvLc56SFLo1H+I3xuUUGywiy17/N8nmnGX/jneFeRJlm3PvJ3y02lj2Pb0h4TNr1\nQnCvm/xeaHGe8LB/n+Q3ga+BjEP6ZL8fxiIzGAwGg8FgMBgMThzmIDMYDAaDwWAwGAxOHI6Fa9la\nIOKaC0BzIVgzW2O2ute97iWpB/tjwvPAbky2mBzdhIuLSKv0jdmMtlvgG24o3hfMc5j+PLlAVo13\ntzNAYBnVlb19TI6YF6UlaJD+ugtJVvj1itLuVrMvuAtO8kIL3m6m4jR3u3me9plTpwuJAJgjd+PD\n/c7deUDypfN3Bnj6+3Alo3/OL5iIWwB6ppx2/uTvNAv735lu0vsOjVuaSZBB7vsC/NZSUAK/tiY7\nMuV4k0Et0QFrM1OJSgsvYY73dQQPZYIFb6u52DI+2nazfN7v1aPPPPPMrXtcLuZY2zrKMfm1rFbu\nbeBW0ZKl7APNhekgbm1r7menSj3cfjuoW1UGbbuLInyW8+I0pM3mPnSQfjas7ZUHcc8Da0HxB3nX\nUaOdhUZYAAAgAElEQVSl5083nCYbcu9osqS1Dc444wxJS2pvv7/JknxPS+CATEbGtlTRLaA/y0L4\nWPiNufVvD/iz8RtjgJdpZ43OvvaTdu3aPtFSljf3+HTnbc8hF31fzDnzti+88EJJSzpspyv7duMt\nXLRawgKft+xnuov7OqZf55133s4YMnW8u4ixh7V08UmrtZAAl33pSubPHcY9eSwyg8FgMBgMBoPB\n4MThWFhkPlk0rVhqmf0EmykF3eqSFhKKTUlLgFY7IXJizQJE0qJB5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i00hp4ug+A9+NNd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RxqMG34ROeyxAeCi0lPfNCsk15s7XIeNpMbFpBfP3ZayTW935HmnpnvN9Li+w4PDd5zE5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\nyZzkrL8K8kAZp2l2Mk1L15n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5z4dqZEoppZRSSilHx0WhkeFt1SUTSE6SIxMSkJnkyj/P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115922eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = [0,1,2,3,4,5]\n",
    "str_emotions = ['angry','scared','happy','sad','surprised','normal']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 6\n",
    "plt.rcParams['figure.figsize'] = (10.0, 10.0) # set default size of plots\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(emotions == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(images[idx])\n",
    "        y_h, x_h = np.histogram( images[idx], hist_div );\n",
    "        plt.axis('off')\n",
    "        if(i == 0):\n",
    "            plt.title(str_emotions[y] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare the Data for CNN\n",
    "Here the initial data have been divided to create train and test data. <br>\n",
    "This two subsets have both an associated label to train the neural network and to test its accuracy with the test data.\n",
    "The number of images used for each category of emotions is shown both for the train as for the test data.<br>\n",
    "The size of each batch it has been chosen to 64 because, after analyzing the performances, we discovered that decreasing the batch size from 100 to 64 actually improved the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of clean data:1053  48x48 pix , 0-255 greyscale images\n",
      "orig train data (989, 48, 48)\n",
      "orig train labels (989,)from 0.0 to 5.0\n",
      "orig test data (64, 48, 48)\n",
      "orig test labels (64,)from 0.0 to 5.0\n",
      "TRAIN: number of 0 labels 192\n",
      "TRAIN: number of 1 labels 149\n",
      "TRAIN: number of 2 labels 192\n",
      "TRAIN: number of 3 labels 174\n",
      "TRAIN: number of 4 labels 111\n",
      "TRAIN: number of 5 labels 171\n",
      "TEST: number of 0 labels 14\n",
      "TEST: number of 1 labels 9\n",
      "TEST: number of 2 labels 10\n",
      "TEST: number of 3 labels 15\n",
      "TEST: number of 4 labels 6\n",
      "TEST: number of 5 labels 10\n"
     ]
    }
   ],
   "source": [
    "print('number of clean data:' + str(images.shape[0]) + '  48x48 pix , 0-255 greyscale images')\n",
    "n_all = images.shape[0];\n",
    "n_train = 64; # number of data for training and for batch\n",
    "\n",
    "# dividing the input data\n",
    "train_data_orig = images[0:n_all-n_train,:,:]\n",
    "train_labels = emotions[0:n_all-n_train]\n",
    "test_data_orig = images[n_all-n_train:n_all,:,:]\n",
    "test_labels = emotions[n_all-n_train:n_all]\n",
    "\n",
    "# Convert to float\n",
    "train_data_orig = train_data_orig.astype('float32')\n",
    "y_train = train_labels.astype('float32')\n",
    "test_data_orig = test_data_orig.astype('float32')\n",
    "y_test = test_labels.astype('float32')\n",
    "\n",
    "print('orig train data ' + str(train_data_orig.shape))\n",
    "print('orig train labels ' + str(train_labels.shape) + 'from ' + str(train_labels.min()) + ' to ' + str(train_labels.max()) )\n",
    "print('orig test data ' + str(test_data_orig.shape))\n",
    "print('orig test labels ' + str(test_labels.shape)+ 'from ' + str(test_labels.min()) + ' to ' + str(test_labels.max()) )\n",
    "\n",
    "for i in range (0, 6):  \n",
    "    print('TRAIN: number of' , i, 'labels',len(train_labels[train_labels == i]))\n",
    "\n",
    "for i in range (0, 6):  \n",
    "    print('TEST: number of', i, 'labels',len(test_labels[test_labels == i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Data Amount per class\n",
    "As we can see, the number of training images for each class is different. For the initial database, the amount of \"happy\" images was even bigger(8k), that is why we decided to skip 3k random \"happy\" class images. This also helps with the speed of execution, since our database it's a bit smaller.<br>\n",
    "In fact, the non homogeneous number of images per class could lead the network to be excessively accurate on one single class rather than on each one.\n",
    "\n",
    "# Prepare the data for CNN\n",
    "Here the data is modified  a little bit to be correctly fed into the CNN. <br>\n",
    "What has been done is convert, normalize and subtract the const mean value from the data images.<br>\n",
    "Finally the label values of the classes are converted to a binary one_hot vector.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YtDyIW4EzpRwcYnDbaAyWrLw/NfvTuYgB+66vXLmCS5cuAbBL46RSKasyOacr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\njjEcHGLgGMPBIQb/D/VnCVk4LJuYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5cfc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data pre-processing\n",
    "n = train_data_orig.shape[0];\n",
    "train_data = np.zeros([n,48**2])\n",
    "for i in range(n):\n",
    "    xx = train_data_orig[i,:,:]\n",
    "    xx -= np.mean(xx)\n",
    "    xx /= np.linalg.norm(xx)\n",
    "    train_data[i,:] = xx.reshape(2304); #np.reshape(xx,[-1])\n",
    "\n",
    "n = test_data_orig.shape[0]\n",
    "test_data = np.zeros([n,48**2])\n",
    "for i in range(n):\n",
    "    xx = test_data_orig[i,:,:]\n",
    "    xx -= np.mean(xx)\n",
    "    xx /= np.linalg.norm(xx)\n",
    "    test_data[i] = np.reshape(xx,[-1])\n",
    "\n",
    "#print(train_data.shape)\n",
    "#print(test_data.shape)\n",
    "#print(train_data_orig[0][2][2])\n",
    "#print(test_data[0][2])\n",
    "plt.rcParams['figure.figsize'] = (2.0, 2.0) # set default size of plots\n",
    "plt.subplot(121);\n",
    "plt.imshow(train_data[9].reshape([48,48]));\n",
    "plt.title(' after ');\n",
    "plt.axis('off')\n",
    "plt.subplot(122);\n",
    "plt.imshow(train_data_orig[9]);\n",
    "plt.title(' before ');\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train labels shape (989, 6)\n",
      "test labels shape (64, 6)\n"
     ]
    }
   ],
   "source": [
    "# Convert label values to one_hot vector\n",
    "\n",
    "train_labels = ut.convert_to_one_hot(train_labels,num_classes)\n",
    "test_labels = ut.convert_to_one_hot(test_labels,num_classes)\n",
    "\n",
    "print('train labels shape',train_labels.shape)\n",
    "print('test labels shape',test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 1 \n",
    "In the first model it has been implemented a baseline softmax classifier using a single convolutional layer and a one fully connected layer. For the initial baseline\n",
    "it has not be used any regularization, dropout, or batch normalization.\n",
    "\n",
    "The equation of the classifier is simply:\n",
    "\n",
    "\n",
    "$$\n",
    "y=\\textrm{softmax}(ReLU( x \\ast W_1+b_1)W_2+b_2) \n",
    "$$\n",
    "\n",
    "For this first attempt have been applied 64 filters with size 8x8.  \n",
    "The optimization scheme chosen if the AdamOptimizer with a learning rate of 0.004 s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wcl= (8, 8, 1, 64)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'bias_variable' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-6a0e747def26>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mWcl0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtruncated_normal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mK0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mK0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mF0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstddev\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_float\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncl0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Wcl='\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mWcl0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mbcl0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbias_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mF0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bcl0='\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbcl0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#in ReLu case, small positive bias added to prevent killing of gradient when input is negative.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m#Reshaping the input to size 48x48\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'bias_variable' is not defined"
     ]
    }
   ],
   "source": [
    "# Define computational graph (CG)\n",
    "batch_size = n_train    # batch size\n",
    "d = train_data.shape[1]  # data dimensionality\n",
    "nc = 6                  # number of classes\n",
    "\n",
    "# Inputs\n",
    "xin = tf.placeholder(tf.float32,[batch_size,d]); \n",
    "y_label = tf.placeholder(tf.float32,[batch_size,nc]); \n",
    "\n",
    "#Size and number of filters\n",
    "K0 = 8   # size of the patch\n",
    "F0 = 64  # number of filters\n",
    "ncl0 = K0*K0*F0\n",
    "\n",
    "Wcl0 = tf.Variable(tf.truncated_normal([K0,K0,1,F0], stddev=tf.sqrt(2./tf.to_float(ncl0)) )); print('Wcl=',Wcl0.get_shape())\n",
    "bcl0 = bias_variable([F0]); print('bcl0=',bcl0.get_shape()) #in ReLu case, small positive bias added to prevent killing of gradient when input is negative.\n",
    "\n",
    "#Reshaping the input to size 48x48 \n",
    "x_2d0 = tf.reshape(xin, [-1,48,48,1]); print('x_2d=',x_2d0.get_shape())\n",
    "\n",
    "# Convolutional layer\n",
    "x = tf.nn.conv2d(x_2d0, Wcl0, strides=[1, 1, 1, 1], padding='SAME')\n",
    "x += bcl0; print('x2=',x.get_shape())\n",
    "\n",
    "# ReLU activation\n",
    "x = tf.nn.relu(x)\n",
    "\n",
    "# Fully Connected layer\n",
    "nfc = 48*48*F0\n",
    "x = tf.reshape(x, [batch_size,-1]); print('x3=',x.get_shape())\n",
    "Wfc = tf.Variable(tf.truncated_normal([nfc,nc], stddev=tf.sqrt(2./tf.to_float(nfc+nc)) )); print('Wfc=',Wfc.get_shape())\n",
    "bfc = tf.Variable(tf.zeros([nc])); print('bfc=',bfc.get_shape())\n",
    "y = tf.matmul(x, Wfc); print('y1=',y.get_shape())\n",
    "y += bfc; print('y2=',y.get_shape())\n",
    "\n",
    "# Softmax\n",
    "y = tf.nn.softmax(y); print('y3(SOFTMAX)=',y.get_shape())\n",
    "\n",
    "# Loss\n",
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))\n",
    "total_loss = cross_entropy\n",
    "\n",
    "# Optimization scheme\n",
    "train_step = tf.train.AdamOptimizer(0.004).minimize(total_loss)\n",
    "\n",
    "# Accuracy\n",
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iteration i= 0 , train accuracy= 0.109375 , loss= 1.7942\n",
      "test accuracy= 0.140625\n",
      "\n",
      "Iteration i= 100 , train accuracy= 0.234375 , loss= 1.71757\n",
      "test accuracy= 0.203125\n",
      "\n",
      "Iteration i= 200 , train accuracy= 0.359375 , loss= 1.66261\n",
      "test accuracy= 0.3125\n",
      "\n",
      "Iteration i= 300 , train accuracy= 0.40625 , loss= 1.55144\n",
      "test accuracy= 0.25\n",
      "\n",
      "Iteration i= 400 , train accuracy= 0.4375 , loss= 1.44691\n",
      "test accuracy= 0.234375\n",
      "\n",
      "Iteration i= 500 , train accuracy= 0.265625 , loss= 1.59834\n",
      "test accuracy= 0.296875\n",
      "\n",
      "Iteration i= 600 , train accuracy= 0.28125 , loss= 1.72735\n",
      "test accuracy= 0.265625\n",
      "\n",
      "Iteration i= 700 , train accuracy= 0.328125 , loss= 1.61648\n",
      "test accuracy= 0.265625\n",
      "\n",
      "Iteration i= 800 , train accuracy= 0.40625 , loss= 1.49183\n",
      "test accuracy= 0.25\n",
      "\n",
      "Iteration i= 900 , train accuracy= 0.421875 , loss= 1.48842\n",
      "test accuracy= 0.28125\n",
      "\n",
      "Iteration i= 1000 , train accuracy= 0.4375 , loss= 1.36199\n",
      "test accuracy= 0.203125\n"
     ]
    }
   ],
   "source": [
    "# Run Computational Graph\n",
    "n = train_data.shape[0]\n",
    "indices = collections.deque()\n",
    "init = tf.initialize_all_variables()\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "for i in range(1001):\n",
    "    \n",
    "    # Batch extraction\n",
    "    if len(indices) < batch_size:\n",
    "        indices.extend(np.random.permutation(n)) \n",
    "    idx = [indices.popleft() for i in range(batch_size)]\n",
    "    batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
    "    #print(batch_x.shape,batch_y.shape)\n",
    "    \n",
    "    # Run CG for vao to increase the test acriable training\n",
    "    _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={xin: batch_x, y_label: batch_y})\n",
    "    \n",
    "    # Run CG for test set\n",
    "    if not i%100:\n",
    "        print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
    "        acc_test = sess.run(accuracy, feed_dict={xin: test_data, y_label: test_labels})\n",
    "        print('test accuracy=',acc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comments\n",
    "The model was overfitting the shortened (1200) data, while getting a test accuracy of only 28%. For the full dataset of 35k images the dataset was not diverging (shown above) and the accuracy was also around 25%. It could not find any features with this architecture. That is why we discarded Model 1 and started to implement more advanced architectures.\n",
    "\n",
    "In order to prevent overfitting in the following models have been applied different techniques such as dropout and pool, as well as tried to implement a neural network of more layers. \n",
    "\n",
    "This should help and improve the model since the first convolutional layer will just extract some simplest characteristics of the image such as edges, lines and curves. Adding layers will improve the performances because they will detect some high level feature which in this case could be really relevant since it's about face expressions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Techniques \n",
    "\n",
    "To prevent the overfitting problem observed with the first model as well as extract as many features as possible from the images, the following techniques have been studied and used.\n",
    "\n",
    "## 1.Choosing Hyperparameters\n",
    "\n",
    "One of the most challenging choise to be done while constructing a convolutional neural network is the choice of the number and dimension of the filter to be used as well as the number of layers to employ.<br>\n",
    "Of course there is not a standard design, because it depends on the dataset and the features of the different images and on the complexity of the task.\n",
    "For our purpose we decided to start with a higher size of filter and then decrease it.\n",
    "\n",
    "Besides, the activation layer that has been employed is the ReLU one (Rectified Linear Units) which is the most used and most efficient since it helps to alleviate the vanishing gradient problem.\n",
    "The ReLU layer applies the non linear function f(x) = max(0, x) to the input basically just eliminating all the negative activations. \n",
    "This design choice also explain why we initialized the bias vector b to a small positive value that is to prevent killing of gradient when input is negative.\n",
    "\n",
    "## 2.Pooling Layers\n",
    "\n",
    "Pool layer could be added after the ReLu ones. This layer is also known as a downsampling layer.\n",
    "The intuitive reasoning behind this layer is that once we know that a specific feature is in the original input volume, its exact location is not as important as its relative location to the other features.\n",
    "This layer drastically reduces the spatial dimensions of the input volume and is used to reduce the computation cost and to control overfitting.\n",
    "\n",
    "## 3.Dropout Layers\n",
    "Finally, dropout layers can be used to deactivate with a defined probability a random set of activations in that specific layer which in the forward pass is then considered as set to zero.\n",
    "Also this technique can help to prevent the overfitting problem. \n",
    "\n",
    "Ref: https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced computational graphs - functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Definition of function that have been used in the CNN\n",
    "\n",
    "d = train_data.shape[1]\n",
    "\n",
    "def weight_variable2(shape, nc10):\n",
    "        initial2 = tf.random_normal(shape, stddev=tf.sqrt(2./tf.to_float(ncl0)) )\n",
    "        return tf.Variable(initial2)\n",
    "    \n",
    "def conv2dstride2(x,W):\n",
    "        return tf.nn.conv2d(x,W,strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "def conv2d(x,W):\n",
    "        return tf.nn.conv2d(x,W,strides=[1, 1, 1, 1], padding='SAME')\n",
    "    \n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "def weight_variable(shape):\n",
    "        initial = tf.truncated_normal(shape, stddev=1/np.sqrt(d/2) )\n",
    "        return tf.Variable(initial)\n",
    "    \n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.01,shape=shape)\n",
    "    return tf.Variable(initial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Different architectures\n",
    "During our research we tried different architectures and tested numerous techniqies to achieve the best possible results. Among others, we experimented with:\n",
    "<br> - 2 to 10 convolutional layers \n",
    "<br> - 1 to 4 fully connected layers in different places in the graph (middle, end, beggining)\n",
    "<br> - different size of patches (from 2 to 16) and filters(from 10 to 64)\n",
    "<br> - max pooling\n",
    "<br> - droupout\n",
    "<br> - L2 regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 2:  7 Layers, Conv-Relu-Maxpool, 1 Fully Connected \n",
    "\n",
    "The third model consists in a 6 layer convolutional neural network with a final fully connected layer.\n",
    "\n",
    "$$\n",
    "x= maxpool2x2( ReLU( ReLU( x* W_1+b_1) * W_2+b_2))$$ 3 times (also for $W_3,b_3 W_4,b_4 W_4,b_4 W_5,b_5 W_6,b_6$) \n",
    "\n",
    "for each layer it has been added a pool layer after the ReLU and this result in a decreasing dimensionality (from 48 to 6)\n",
    "\n",
    "$$\n",
    "y=\\textrm{softmax} {( x W_{norm6}+b_{norm6})}$$\n",
    "\n",
    "For the 1,2,3 layers the filter used are 22 with a dimension of 8x8 while for the 4,5,6 a dimension of 4x4.\n",
    "A dropout layer is also applied.\n",
    "The optimization scheme used it AdamOptimizer with a learning rate of 0.001 s. The dropout probability has been set to 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W_conv1= (8, 8, 1, 22)\n",
      "b_conv1= (22,)\n",
      "x_2d1= (64, 48, 48, 1)\n",
      "h_conv1= (64, 48, 48, 22)\n",
      "W_conv2= (8, 8, 22, 22)\n",
      "b_conv2= (22,)\n",
      "h_conv2= (64, 48, 48, 22)\n",
      "h_conv2_pooled= (64, 24, 24, 22)\n",
      "W_conv3= (8, 8, 22, 22)\n",
      "b_conv3= (22,)\n",
      "x_2d3= (64, 24, 24, 22)\n",
      "h_conv3= (64, 24, 24, 22)\n",
      "W_conv4= (4, 4, 22, 22)\n",
      "b_conv4= (22,)\n",
      "h_conv4= (64, 24, 24, 22)\n",
      "h_conv4_pooled= (64, 12, 12, 22)\n",
      "h_conv4_pooled= (64, 6, 6, 22)\n",
      "W_conv5= (4, 4, 22, 22)\n",
      "b_conv5= (22,)\n",
      "x_2d5= (64, 6, 6, 22)\n",
      "h_conv5= (64, 6, 6, 22)\n",
      "W_con6= (4, 4, 22, 22)\n",
      "b_conv6= (22,)\n",
      "h_conv6= (64, 6, 6, 22)\n",
      "h_conv6_pooled= (64, 3, 3, 22)\n",
      "x2_rs (64, 792)\n",
      "W_norm6= (792, 6)\n",
      "b_conv6= (6,)\n",
      "h_full6= (64, 6)\n",
      "h_full6= (64, 6)\n",
      "y3(SOFTMAX)= (64, 6)\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# implementation of Conv-Relu-COVN-RELU - pool\n",
    "# based on : http://cs231n.github.io/convolutional-networks/\n",
    "\n",
    "# Define computational graph (CG)\n",
    "batch_size = n_train    # batch size\n",
    "d = train_data.shape[1]  # data dimensionality\n",
    "nc = 6                  # number of classes\n",
    "\n",
    "# Inputs\n",
    "xin = tf.placeholder(tf.float32,[batch_size,d]); #print('xin=',xin,xin.get_shape())\n",
    "y_label = tf.placeholder(tf.float32,[batch_size,nc]); #print('y_label=',y_label,y_label.get_shape())\n",
    "\n",
    "\n",
    "#for the first conc-conv\n",
    "# Convolutional layer\n",
    "K0 = 8   # size of the patch\n",
    "F0 = 22  # number of filters\n",
    "ncl0 = K0*K0*F0\n",
    "\n",
    "#for the second conc-conv\n",
    "K1 = 4   # size of the patch\n",
    "F1 = F0  # number of filters\n",
    "ncl1 = K1*K1*F1\n",
    "\n",
    "#drouput probability\n",
    "keep_prob_input=tf.placeholder(tf.float32)\n",
    "\n",
    "#1st set of conv followed by conv2d operation and dropout 0.5\n",
    "W_conv1=weight_variable([K0,K0,1,F0]); print('W_conv1=',W_conv1.get_shape())\n",
    "b_conv1=bias_variable([F0]); print('b_conv1=',b_conv1.get_shape())\n",
    "x_2d1 = tf.reshape(xin, [-1,48,48,1]); print('x_2d1=',x_2d1.get_shape())\n",
    "\n",
    "#conv2d \n",
    "h_conv1=tf.nn.relu(conv2d(x_2d1, W_conv1) + b_conv1); print('h_conv1=',h_conv1.get_shape())\n",
    "#h_conv1= tf.nn.dropout(h_conv1,keep_prob_input);\n",
    "\n",
    "# 2nd convolutional layer + max pooling\n",
    "W_conv2=weight_variable([K0,K0,F0,F0]); print('W_conv2=',W_conv2.get_shape())\n",
    "b_conv2=bias_variable([F0]); print('b_conv2=',b_conv2.get_shape())\n",
    "\n",
    "# conv2d + max pool\n",
    "h_conv2 = tf.nn.relu(conv2d(h_conv1,W_conv2)+b_conv2); print('h_conv2=',h_conv2.get_shape())\n",
    "h_conv2_pooled = max_pool_2x2(h_conv2); print('h_conv2_pooled=',h_conv2_pooled.get_shape())\n",
    "\n",
    "#3rd set of conv \n",
    "W_conv3=weight_variable([K0,K0,F0,F0]); print('W_conv3=',W_conv3.get_shape())\n",
    "b_conv3=bias_variable([F1]); print('b_conv3=',b_conv3.get_shape())\n",
    "x_2d3 = tf.reshape(h_conv2_pooled, [-1,24,24,F0]); print('x_2d3=',x_2d3.get_shape())\n",
    "\n",
    "#conv2d\n",
    "h_conv3=tf.nn.relu(conv2d(x_2d3, W_conv3) + b_conv3); print('h_conv3=',h_conv3.get_shape())\n",
    "\n",
    "# 4th convolutional layer \n",
    "W_conv4=weight_variable([K1,K1,F1,F1]); print('W_conv4=',W_conv4.get_shape())\n",
    "b_conv4=bias_variable([F1]); print('b_conv4=',b_conv4.get_shape())\n",
    "\n",
    "#conv2d + max pool 4x4\n",
    "h_conv4 = tf.nn.relu(conv2d(h_conv3,W_conv4)+b_conv4); print('h_conv4=',h_conv4.get_shape())\n",
    "h_conv4_pooled = max_pool_2x2(h_conv4); print('h_conv4_pooled=',h_conv4_pooled.get_shape())\n",
    "h_conv4_pooled = max_pool_2x2(h_conv4_pooled); print('h_conv4_pooled=',h_conv4_pooled.get_shape())\n",
    "\n",
    "#5th set of conv \n",
    "W_conv5=weight_variable([K1,K1,F1,F1]); print('W_conv5=',W_conv5.get_shape())\n",
    "b_conv5=bias_variable([F1]); print('b_conv5=',b_conv5.get_shape())\n",
    "x_2d5 = tf.reshape(h_conv4_pooled, [-1,6,6,F1]); print('x_2d5=',x_2d5.get_shape())\n",
    "\n",
    "#conv2d\n",
    "h_conv5=tf.nn.relu(conv2d(x_2d5, W_conv5) + b_conv5); print('h_conv5=',h_conv5.get_shape())\n",
    "\n",
    "# 6th convolutional layer \n",
    "W_conv6=weight_variable([K1,K1,F1,F1]); print('W_con6=',W_conv6.get_shape())\n",
    "b_conv6=bias_variable([F1]); print('b_conv6=',b_conv6.get_shape())\n",
    "b_conv6= tf.nn.dropout(b_conv6,keep_prob_input);\n",
    "\n",
    "#conv2d + max pool 4x4\n",
    "h_conv6 = tf.nn.relu(conv2d(h_conv5,W_conv6)+b_conv6); print('h_conv6=',h_conv6.get_shape())\n",
    "h_conv6_pooled = max_pool_2x2(h_conv6); print('h_conv6_pooled=',h_conv6_pooled.get_shape())\n",
    "\n",
    "# reshaping for fully connected\n",
    "h_conv6_pooled_rs = tf.reshape(h_conv6, [batch_size,-1]); print('x2_rs',h_conv6_pooled_rs.get_shape());\n",
    "W_norm6 = weight_variable([  6*6*F1, nc]); print('W_norm6=',W_norm6.get_shape())\n",
    "b_norm6 = bias_variable([nc]); print('b_conv6=',b_norm6.get_shape())\n",
    "\n",
    "# fully connected layer\n",
    "h_full6 = tf.matmul( h_conv6_pooled_rs, W_norm6 ); print('h_full6=',h_full6.get_shape())\n",
    "h_full6 += b_norm6; print('h_full6=',h_full6.get_shape())\n",
    "\n",
    "y = h_full6; \n",
    "\n",
    "## Softmax\n",
    "y = tf.nn.softmax(y); print('y3(SOFTMAX)=',y.get_shape())\n",
    "\n",
    "# Loss\n",
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))\n",
    "total_loss = cross_entropy\n",
    "\n",
    "# Optimization scheme\n",
    "train_step = tf.train.AdamOptimizer(0.001).minimize(total_loss)\n",
    "\n",
    "# Accuracy\n",
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iteration i= 0 , train accuracy= 0.875 , loss= 0.625207\n",
      "test accuracy= 0.546875\n",
      "\n",
      "Iteration i= 100 , train accuracy= 0.828125 , loss= 0.612367\n",
      "test accuracy= 0.4375\n",
      "\n",
      "Iteration i= 200 , train accuracy= 0.78125 , loss= 0.575974\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 300 , train accuracy= 0.890625 , loss= 0.400036\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 400 , train accuracy= 0.875 , loss= 0.340994\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 500 , train accuracy= 0.953125 , loss= 0.223442\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 600 , train accuracy= 0.84375 , loss= 0.406189\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 700 , train accuracy= 0.8125 , loss= 0.611783\n",
      "test accuracy= 0.4375\n",
      "\n",
      "Iteration i= 800 , train accuracy= 0.78125 , loss= 0.754387\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 900 , train accuracy= 0.828125 , loss= 0.485354\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 1000 , train accuracy= 0.90625 , loss= 0.300465\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 1100 , train accuracy= 0.84375 , loss= 0.43599\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 1200 , train accuracy= 0.875 , loss= 0.329349\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 1300 , train accuracy= 0.84375 , loss= 0.366036\n",
      "test accuracy= 0.4375\n",
      "\n",
      "Iteration i= 1400 , train accuracy= 0.890625 , loss= 0.306158\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 1500 , train accuracy= 0.859375 , loss= 0.433374\n",
      "test accuracy= 0.546875\n",
      "\n",
      "Iteration i= 1600 , train accuracy= 0.859375 , loss= 0.308801\n",
      "test accuracy= 0.4375\n",
      "\n",
      "Iteration i= 1700 , train accuracy= 0.875 , loss= 0.409247\n",
      "test accuracy= 0.546875\n",
      "\n",
      "Iteration i= 1800 , train accuracy= 0.890625 , loss= 0.380305\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 1900 , train accuracy= 0.78125 , loss= 0.520572\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 2000 , train accuracy= 0.796875 , loss= 0.485217\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 2100 , train accuracy= 0.875 , loss= 0.431877\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 2200 , train accuracy= 0.875 , loss= 0.357166\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 2300 , train accuracy= 0.8125 , loss= 0.477087\n",
      "test accuracy= 0.421875\n",
      "\n",
      "Iteration i= 2400 , train accuracy= 0.84375 , loss= 0.447944\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 2500 , train accuracy= 0.859375 , loss= 0.388267\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 2600 , train accuracy= 0.828125 , loss= 0.371235\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 2700 , train accuracy= 0.8125 , loss= 0.453498\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 2800 , train accuracy= 0.8125 , loss= 0.450164\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 2900 , train accuracy= 0.78125 , loss= 0.48953\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 3000 , train accuracy= 0.84375 , loss= 0.390099\n",
      "test accuracy= 0.546875\n",
      "\n",
      "Iteration i= 3100 , train accuracy= 0.84375 , loss= 0.318438\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 3200 , train accuracy= 0.9375 , loss= 0.238999\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 3300 , train accuracy= 0.78125 , loss= 0.551882\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 3400 , train accuracy= 0.859375 , loss= 0.301291\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 3500 , train accuracy= 0.890625 , loss= 0.33801\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 3600 , train accuracy= 0.78125 , loss= 0.508257\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 3700 , train accuracy= 0.8125 , loss= 0.471243\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 3800 , train accuracy= 0.859375 , loss= 0.410303\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 3900 , train accuracy= 0.875 , loss= 0.313941\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 4000 , train accuracy= 0.875 , loss= 0.263522\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 4100 , train accuracy= 0.828125 , loss= 0.498695\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 4200 , train accuracy= 0.828125 , loss= 0.446249\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 4300 , train accuracy= 0.875 , loss= 0.404933\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 4400 , train accuracy= 0.921875 , loss= 0.214639\n",
      "test accuracy= 0.546875\n",
      "\n",
      "Iteration i= 4500 , train accuracy= 0.84375 , loss= 0.371443\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 4600 , train accuracy= 0.90625 , loss= 0.248604\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 4700 , train accuracy= 0.8125 , loss= 0.41222\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 4800 , train accuracy= 0.875 , loss= 0.213161\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 4900 , train accuracy= 0.90625 , loss= 0.217379\n",
      "test accuracy= 0.546875\n",
      "\n",
      "Iteration i= 5000 , train accuracy= 0.84375 , loss= 0.378817\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 5100 , train accuracy= 0.78125 , loss= 0.442884\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 5200 , train accuracy= 0.859375 , loss= 0.396165\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 5300 , train accuracy= 0.8125 , loss= 0.468761\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 5400 , train accuracy= 0.90625 , loss= 0.227272\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 5500 , train accuracy= 0.875 , loss= 0.276343\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 5600 , train accuracy= 0.890625 , loss= 0.44843\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 5700 , train accuracy= 0.84375 , loss= 0.487646\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 5800 , train accuracy= 0.875 , loss= 0.315235\n",
      "test accuracy= 0.421875\n",
      "\n",
      "Iteration i= 5900 , train accuracy= 0.84375 , loss= 0.371654\n",
      "test accuracy= 0.546875\n",
      "\n",
      "Iteration i= 6000 , train accuracy= 0.796875 , loss= 0.447215\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 6100 , train accuracy= 0.890625 , loss= 0.325186\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 6200 , train accuracy= 0.859375 , loss= 0.419543\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 6300 , train accuracy= 0.828125 , loss= 0.39386\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 6400 , train accuracy= 0.890625 , loss= 0.352319\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 6500 , train accuracy= 0.875 , loss= 0.301503\n",
      "test accuracy= 0.421875\n",
      "\n",
      "Iteration i= 6600 , train accuracy= 0.859375 , loss= 0.378798\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 6700 , train accuracy= 0.890625 , loss= 0.26334\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 6800 , train accuracy= 0.78125 , loss= 0.466438\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 6900 , train accuracy= 0.921875 , loss= 0.168848\n",
      "test accuracy= 0.40625\n",
      "\n",
      "Iteration i= 7000 , train accuracy= 0.921875 , loss= 0.242757\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 7100 , train accuracy= 0.859375 , loss= 0.295266\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 7200 , train accuracy= 0.859375 , loss= 0.457388\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 7300 , train accuracy= 0.890625 , loss= 0.230857\n",
      "test accuracy= 0.421875\n",
      "\n",
      "Iteration i= 7400 , train accuracy= 0.953125 , loss= 0.243335\n",
      "test accuracy= 0.40625\n",
      "\n",
      "Iteration i= 7500 , train accuracy= 0.875 , loss= 0.270458\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 7600 , train accuracy= 0.890625 , loss= 0.379422\n",
      "test accuracy= 0.40625\n",
      "\n",
      "Iteration i= 7700 , train accuracy= 0.828125 , loss= 0.550814\n",
      "test accuracy= 0.4375\n",
      "\n",
      "Iteration i= 7800 , train accuracy= 0.96875 , loss= 0.12204\n",
      "test accuracy= 0.421875\n",
      "\n",
      "Iteration i= 7900 , train accuracy= 0.84375 , loss= 0.414909\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 8000 , train accuracy= 0.859375 , loss= 0.412397\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 8100 , train accuracy= 0.828125 , loss= 0.367784\n",
      "test accuracy= 0.4375\n",
      "\n",
      "Iteration i= 8200 , train accuracy= 0.859375 , loss= 0.499167\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 8300 , train accuracy= 0.921875 , loss= 0.206405\n",
      "test accuracy= 0.515625\n",
      "\n",
      "Iteration i= 8400 , train accuracy= 0.796875 , loss= 0.497307\n",
      "test accuracy= 0.4375\n",
      "\n",
      "Iteration i= 8500 , train accuracy= 0.890625 , loss= 0.268824\n",
      "test accuracy= 0.546875\n",
      "\n",
      "Iteration i= 8600 , train accuracy= 0.84375 , loss= 0.334944\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 8700 , train accuracy= 0.96875 , loss= 0.133828\n",
      "test accuracy= 0.5625\n",
      "\n",
      "Iteration i= 8800 , train accuracy= 0.9375 , loss= 0.189325\n",
      "test accuracy= 0.5\n",
      "\n",
      "Iteration i= 8900 , train accuracy= 0.921875 , loss= 0.301729\n",
      "test accuracy= 0.4375\n",
      "\n",
      "Iteration i= 9000 , train accuracy= 0.90625 , loss= 0.414903\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 9100 , train accuracy= 0.875 , loss= 0.288617\n",
      "test accuracy= 0.421875\n",
      "\n",
      "Iteration i= 9200 , train accuracy= 0.828125 , loss= 0.483817\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 9300 , train accuracy= 0.921875 , loss= 0.239591\n",
      "test accuracy= 0.421875\n",
      "\n",
      "Iteration i= 9400 , train accuracy= 0.84375 , loss= 0.359176\n",
      "test accuracy= 0.46875\n",
      "\n",
      "Iteration i= 9500 , train accuracy= 0.859375 , loss= 0.372064\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 9600 , train accuracy= 0.9375 , loss= 0.180962\n",
      "test accuracy= 0.53125\n",
      "\n",
      "Iteration i= 9700 , train accuracy= 0.859375 , loss= 0.414258\n",
      "test accuracy= 0.484375\n",
      "\n",
      "Iteration i= 9800 , train accuracy= 0.875 , loss= 0.376626\n",
      "test accuracy= 0.453125\n",
      "\n",
      "Iteration i= 9900 , train accuracy= 0.9375 , loss= 0.264636\n",
      "test accuracy= 0.390625\n"
     ]
    }
   ],
   "source": [
    "# Run Computational Graph\n",
    "n = train_data.shape[0]\n",
    "indices = collections.deque()\n",
    "#init = tf.global_variables_initializer()\n",
    "#sess = tf.Session()\n",
    "#sess.run(init)\n",
    "for i in range(10000):\n",
    "    \n",
    "    # Batch extraction\n",
    "    if len(indices) < batch_size:\n",
    "        indices.extend(np.random.permutation(n)) \n",
    "    idx = [indices.popleft() for i in range(batch_size)]\n",
    "    batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
    "    #print(batch_x.shape,batch_y.shape)\n",
    "    \n",
    "    # Run CG for vao to increase the test acriable training\n",
    "    _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={xin: batch_x, y_label: batch_y, keep_prob_input: 1.0})\n",
    "    \n",
    "    # Run CG for test set\n",
    "    if not i%100:\n",
    "        print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
    "        acc_test = sess.run(accuracy, feed_dict = {xin: test_data, y_label: test_labels, keep_prob_input: 1.0})\n",
    "        print('test accuracy=',acc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 2- Results\n",
    "\n",
    "This second model reaches a test accuracy up to 56% which is the best result we could get.\n",
    "We can also see that we have prevented the overfitting problem observed with the previous models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the trained graph in TF file\n",
    "Ref:https://www.tensorflow.org/how_tos/variables/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sess' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-4321ba6fc0da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Add ops to save and restore all the variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m# Save the variables to disk.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msave_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'model_6layers_after30k.ckpt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sess' is not defined"
     ]
    }
   ],
   "source": [
    "# Add ops to save and restore all the variables.\n",
    "saver = tf.train.Saver()\n",
    "sess.run(init)\n",
    "# Save the variables to disk.\n",
    "save_path = saver.save(sess, 'model_6layers_after30k.ckpt')\n",
    "print(\"Model saved in file: %s\" % save_path)\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Model from TF File\n",
    "Ref:https://www.tensorflow.org/how_tos/variables/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "# Add ops to save and restore all the variables.\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "saver.restore(sess, './model_6layers.ckpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Class Accuracy\n",
    "\n",
    "To see how our network behaves for every classes, it is calculated separately the accuracy for each of them.<br>\n",
    "As we can see it is slightly different for each emotion, with a highest accuracy for the surprised one (72%) and a lowest for the sad one (31%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy angry    \t 0.8666666666666667\n",
      "accuracy scared    \t 0.875\n",
      "accuracy happy    \t 0.7368421052631579\n",
      "accuracy sad    \t 0.9032258064516129\n",
      "accuracy surprised    \t 0.9090909090909091\n",
      "accuracy normal    \t 0.7619047619047619\n"
     ]
    }
   ],
   "source": [
    "# calculating accuracy for each class separately for the test set\n",
    "result_cnn = sess.run([y], feed_dict = {xin: test_data,  keep_prob_input: 1.0})\n",
    "#result = sess.run(y, feed_dict={xin: test_data, keep_prob_input: 1.0})\n",
    "\n",
    "tset = test_labels.argmax(1);\n",
    "result = np.asarray(result_cnn[:][0]).argmax(1);\n",
    "\n",
    "for i in range (0,nc):\n",
    "    print('accuracy',str_emotions[i]+str('   '), '\\t',ut.calc_partial_accuracy(tset, result, i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring CNN Filters\n",
    "It is possible to explore what did our CNN learn by extracting the W matrixes from the model. We could observe, that each 1st layer contains some general filters recognizing different parts of the face and the next layers using this data to extract different emotions. We could also observe, that some filters were not well developed (noise). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EpunTp4vcsGFDka+4Qg7L5cuXO219CUuLFi1Erl27tsjz5s0L2k2Ekb4d4Zo1a0TesGGD\nyO7lSCNHjhS1rKwskfWtNAcNGhS4nwAAFDbujAMAAACGMBkHAAAADGEyDgAAABgS8jXjDz74oMid\nOnUSedeuXSK7t7F77LHHRE3fylBfG1qyZMmg3UQY6dsNrlixQuShQ4eKnJSU5LT1tcX6FmVPPvmk\nyCVKlAjcT4SPvk3dxo0bRb7yyitFLlWqlOdrJScni3z8+HGRJ0+eLPKoUaN+dj8RPvrPe53f1oaX\n2haxXbt2gfoEszp37uxZu9R2lvoWum5+W9whso0YMcKzpm+Zq8vMzPSs+W2FqZRSPXr08O9YAXFn\nHAAAADCEyTgAAABgCJNxAAAAwJCQrxk/d+6cyPp64IULF4pctWpVp62vC42JiRHZvZZYKaUOHz4s\nsn50OiKDvq67TJkyIut7ibvr+/fvFzV9/OjH32ZkZATtJsJI32u+T58+IuvPCvzwww9OOz09XdQO\nHjwosr7+nKPQAQCRhDvjAAAAgCFMxgEAAABDmIwDAAAAhli2bZvuAwAAAPCbxJ1xAAAAwBAm4wAA\nAIAhTMYBAAAAQ5iMAwAAAIYwGQcAAAAMCfkJnK+++qrYriUnJ0fU9RMUd+3a5bSLFSsmatWrVxe5\nadOmImdlZYk8depUq4DdRRiMGzdOjIl9+/aJ+qFDh0Ru0KCB065UqZKo1a5dW2T9JMeXXnpJ5Dlz\n5jAmIlCHDh3EmNC/b998843ICxYscNrXX3+9qEVHR4usn8qanZ0t8tixYxkTEahJkya+W339+c9/\n9qy9//77vq+9aNEi37pt24yJyOQ5Jvr27et74Q033OBZK1LE/77k3XffzXiIUA0aNPAcE/Xq1fO9\ntlq1ap61Dh06+F572223FeqY4M44AAAAYAiTcQAAAMCQkC9TuXDhgsjut5eVUmr+/PkilytXzmnX\nr19f1Jo0aeL7e5UtWzZIFxFmzz//vMhPPPGEyMWLFxf5nXfe+cm2Ukr9/e9/F7lWrVoib9q0KXA/\nET7333+/yElJSSJPnTpV5FOnTjlt/a3IhIQEkUuWLCmyvtQJAACTuDMOAAAAGMJkHAAAADCEyTgA\nAABgSMjXjM+bN09kfY14qVKlRK5bt67T/sMf/iBqcXFxIp84cULkokWLBu4nwmfw4MEiZ2RkiKxv\nf5mWlua0p0yZImo33nijyOPGjRO5TJkygfuJ8LFtuTvV4cOHRT5z5ozI7nXf58+fFzX92ZLExESR\n9S1QEZkGDhzoW9d/Trj17NnT99o//vGPgfoEs9544w3P2tGjR32vrVChgmfN/awaLi+PPvqoZy0l\nJcX3Wn37bLfGjRsH7lMQ3BkHAAAADGEyDgAAABjCZBwAAAAwJORrxpOTk0XWj7TXj7Dt06eP09bX\nk+vHpKenp4t8/PjxwP1E+HTr1k1kfb2wvgb45ptvdtr62mF9z3L9aPR+/fqJPHLkyIJ1FmHxr3/9\nS2R9DWdsbKzI27Ztc9pVq1YVtdTUVJH1ZxIsS55i3LFjx4J1FgCAQsSdcQAAAMAQJuMAAACAIUzG\nAQAAAENCvma8WbNmIut7fQ4YMMDz87ds2SJqZ8+eFVnfV1xfC4rIdPLkSZFXr14t8unTp0V+5513\nnPaoUaNE7e233xb5pptuEnnnzp2B+4nwmT17tsg1atQQWX+u4KqrrnLaJUuWFDX9PAL3PvVKKRUV\nFRW4nwgfvz2AlfrvZ4bcPvzwQ99rX3jhhUB9glkLFy70rJUuXdr3WvfZBLrnnnvO99oePXr4dwzG\nTJ061bPWpUsX32ujo6M9a5eaO7Rt29a/YwXEnXEAAADAECbjAAAAgCEhX6bSqVMnkfW3ivS3o93b\nGVauXFnU9uzZI3KRIvxb4nJ05MgRkfVjre+8806R3UejV6lSRdSOHTsm8syZM0WuV69e4H4ifFq2\nbCnyNddcI7K+rWn58uWdtn5scZkyZUTWt0WsW7du4H4CAFDYmM0CAAAAhjAZBwAAAAxhMg4AAAAY\nEvI14+3atRP54sWLIicmJop86tQpp52cnCxqubm5Iutb4J07dy5wPxE++laG+rMBr7/+usifffaZ\n09a3M/v3v/8tsr6GfOLEiYH7ifAZM2aMyGvWrBE5Pj5eZPe2prVr1xa18+fPi1y2bFmReY7g8vDA\nAw/41lesWOFZ2717t++1Y8eO9a1v3rzZtw4z9D/LbvrfBTq/8dSrV6/AfYJZzzzzjGetQ4cOvtfq\nz6e5PfTQQ4H7FAR3xgEAAABDmIwDAAAAhjAZBwAAAAwJ+ZrxlJQUkfXjRz/55BOR3evC9ePus7Oz\nRa5evbrI+jHYiEz6Gl/9uYKtW7eK7F679dZbb4laxYoVRa5QoYLIy5YtE7lr164F6ivC4+TJkyLr\nRw1v2LBBZPc+4/q+9RkZGSLr33P997rUMdoAAIQSd8YBAAAAQ5iMAwAAAIYwGQcAAAAMsWzbNt0H\nAAAA4DeJO+MAAACAIUzGAQAAAEOYjAMAAACGMBkHAAAADAn5oT//+Mc/xBOiFy9eFPUyZcqInJaW\n5vla+rX6YR36IUGjR4+2fn5PES7PPPOMGBPx8fGiro8Jt7Nnz4q8Y8cOke+44w6RK1euLHLx4sUZ\nExFo+vTpvk+Snz59WmT3971IEXlPYffu3SLrB0FdeeWVIo8aNYoxEYF69+7tOyYaNmzoWXvxxRd9\nX7tXr16+9S+//JIxEYHuvfdezzFRqVIl32v37dvnWUtKSvK9duHChYyHCLVgwQLPMZGVleV77dq1\naz1r48eP9702KiqqUMcEd8YBAAAAQ5iMAwAAAIaEfJlKTk6OyBkZGSLrS0vcbzkXK1ZM1EqUKOF7\nbUxMTOB+Iny6du0q8ksvvSRyamqqyCVLlnTaX3zxhaiVLVtW5GnTpol89dVXi/zBBx8UqK8Ij+Tk\nZJFTUlJEPn/+vMg9evRw2hs2bBA1/edA7969RT58+HDgfgIAUNi4Mw4AAAAYwmQcAAAAMITJOAAA\nAGBIyNeMJyYmiqxvKxYdHS2ye/tCfb15qVKlRM7OzhZZX2OOyHTmzBmRq1atKrL+ffzwww+ddrly\n5UStdu3aIuvrgfW1yIhMp06dErlGjRoip6eni3zy5Emn/T//8z+iVrFiRZH17S3Hjh0buJ8InwED\nBvjWv/32W8/a8OHDfa9t1KhRoD7BLP3nvdvx48d9r9W3xXXz2yYTke3777/3rOnPGeomTJjgWVu8\neLHvtd27d/fvWAFxZxwAAAAwhMk4AAAAYAiTcQAAAMCQkK8Zv+IK+Vvo64HPnTsnsnvd1/bt20XN\nvZ5cKaXq1asncp06dQL3E+GzcOFCkTt27CjyihUrRL777ruddrVq1URNX2us70ddvnz5wP1E+Hz5\n5ZciHzlyROTSpUuL3LRpU6fdoEEDUdPXA+t708+dO1fkF154oWCdBQCgEHFnHAAAADCEyTgAAABg\nCJNxAAAAwJCQrxlfvXq1yPre4Vu2bBF58+bNTlvfRzwuLk7kTp06idyqVSuRO3fuXLDOIiyeeuop\nkfv27Stybm6uyO415frewiNGjBBZf65gxowZgfuJ8Pnd734nclRUlMht27YV2bZtp62fXaDvJ5yZ\nmSly0aJFA/cT4aOv7de1b9/es7Zu3Trfa5977jnf+j333ONbhxlXXXWVZ23Pnj2+11avXt2zVrx4\n8cB9glnuMyd0s2fP9r3W7/uun18RatwZBwAAAAxhMg4AAAAYEvJlKmXLlhW5QoUKvvXrrrvOaetv\nJ2dkZPj+XmlpaUG6iDC76aabRNbHwKJFi0Ru3bq10/7uu+9ELSEhQeSkpCSRs7KyAvcT4VOlShWR\n9W1LN2zYIPK2bductr4koXHjxiLry1TS09MD9xMAgMLGnXEAAADAECbjAAAAgCFMxgEAAABDQr5m\nXD+aukSJEiLrx5u71wdbliVq+trPffv2ibx06dKg3UQY6WPixIkTIt96660iHzt2zGkPHz5c1A4c\nOCBybGysyKNGjQrcT4TP8uXLRZ4wYYLItWrVErl06dJOu06dOqJ24cIFkbdv3y6y/jMIkUnfAlWn\nPxvgNmXKFN9r4+PjA/UJZnXo0MGz1rBhQ99ra9So4Vn78ssvA/cJZnXv3t2z9te//tX32lKlSnnW\n/t//+3+B+xQEd8YBAAAAQ5iMAwAAAIYwGQcAAAAMCfmacX3vb32Nb8mSJUVetWqV09aPxD5//rzI\n+trQIkX4t8Xl4JlnnhHZfbS5Ukp169ZNZPdawDNnzvhe+9BDD4ms71+NyNS0aVORZ8yYIXKlSpVE\nvu+++5y2viZ88eLFIuv7ivutEwQAINyYvQIAAACGMBkHAAAADGEyDgAAABhi6WtuAQAAAIQHd8YB\nAAAAQ5iMAwAAAIYwGQcAAAAMYTIOAAAAGMJkHAAAADAk5CdwDhw4UGzXUrx4cVGvU6eOyFdeeaXT\nPn78uKht2LBB5GPHjokcHx8v8sSJE60CdhdhkJWVJcaEfiJi2bJlRXafqrlnzx5Re+qpp0ROSUkR\n+fnnnxd5+vTpjIkIdM8994gxoZ+4+eGHH4rcs2dPp71o0SJR6927t8jNmjUTuUmTJiI3b96cMRGB\nrr32Wt+tvrp37+5Zu+mmm3xfWx8juv379zMmItDIkSM9x8Qrr7zie+0nn3ziWdNPdtbdeuutjIcI\nNX78eM8x0apVK99rd+7c6Vlr0KCB77X9+vUr1DHBnXEAAADAECbjAAAAgCEhX6aiLx1JTU0VuUSJ\nEiKfO3fOaWdnZ4va4cOHRT569KjIp0+fDtxPhM/kyZNFLlOmjMh9+/YV2b08adOmTaL20ksvibx0\n6VKR69WrF7SbCKPhw4eLrC9R03+ONGzY0GnXr19f1NavXy/ywYMHRd6/f7/IzZs3L1BfAQAoTNwZ\nBwAAAAxhMg4AAAAYwmQcAAAAMCTka8ZHjhwp8pEjR0QuVqyYyO5137Ytd6zRt0UsX768yFWrVg3c\nT4TP66+/LnJaWprIAwYMEPmWW25x2rfffruotWzZUuS6deuKvGPHjqDdRBg1btxY5O3bt4vcqFEj\nkd1/1vVtyfTP/fjjj0XWn1FAZNLHgG7NmjWeteXLl/teO3jw4EB9glnDhg3zrM2cOdP3Wv0ZNDd9\nO11cPvr16+dZ07e/1vn9jLn77rsD9ykI7owDAAAAhjAZBwAAAAxhMg4AAAAYEvI14++//77I69at\nE/n8+fMinzx50mmfOnVK1IoWLSqyvj8w674uD+XKlRP5wQcfFDkhIUHk++67z2nre5C/+OKLIl99\n9dUiT506VeS///3vBessjChVqpTI48ePF7ljx45Ou0aNGr6vpe9DXrt27V/YOwAACg93xgEAAABD\nmIwDAAAAhjAZBwAAAAwJ+ZrxN998U+TDhw+LrK8Dj4uLc9rdu3cXtcqVK4scExMjsmVZgfuJ8Jkx\nY4bIf/7zn0WeN2+eyLNmzXLaS5YsEbU//elPIj/33HMi62vMEZmWLVsm8vTp00VOSUkR2f1zY+HC\nhaK2cuVKkaOjo0X+61//GrSbCKNvvvnGt+73rMAXX3zhe+2NN94YqE8wa9OmTZ61f/7zn77X1qpV\ny7OmzzVw+dDnC26PPvqo77Uvv/yyZ+3+++/3vfa1117z71gBcWccAAAAMITJOAAAAGBIyJepTJgw\nQeTY2FiR4+PjRXa/9RgVFSVqV1whu7tv3z6R9be6EZmefvppkQcNGiTy5MmTRW7YsKHTTk1NFTV9\nfE2ZMkXkm266SWR9+QMiQ2Jiosh16tQRuUuXLiJXr17daV+8eFHU9uzZI3LFihVFPnv2bOB+AgBQ\n2LgzDgAAABjCZBwAAAAwhMk4AAAAYEjI14yXKFFCZH19p75m3L12NDc3V9TS09NF1rcy1Lc+RGQa\nN26cyMeOHRN58eLFIr/wwgtO273NoVJKJSQkiKyvKde31kRkmj9/vsjLly8XeezYsSK7nw/Rx0ur\nVq1Evvrqq0UuU6ZM4H4ifPy2HVNKqW7dunnW9DGhmzt3bqA+wayqVat61nr16uV77euvv+5Z++ST\nT3yvHT16tH/HYIzf9sWPPPKI77X79+/3rP3rX/8K2qVAuDMOAAAAGMJkHAAAADCEyTgAAABgSMjX\njD/xxBN85gBiAAAgAElEQVQiHz16VORy5cqJ3L59e6ddpUoVUTtx4oTIJ0+eFFlfQz5kyJCCdRZh\n0bFjR5H1I2uTk5NFfv755512TEyMqN16660iZ2VliXzkyJHA/UT46N83fd33nDlzRHYfRVypUiVR\n0/exP3PmjMj6emF9PAIAEE7cGQcAAAAMYTIOAAAAGMJkHAAAADDEsm3bdB8AAACA3yTujAMAAACG\nMBkHAAAADGEyDgAAABjCZBwAAAAwJOSH/mzZskU8IVq0aFFR1x8gddcbNGggavrhHVu2bBE5Oztb\n5C5dushTgBARkpOTxTe9WrVqov7111+L7P6+ZmRkiJp+MFRqaqrILVu2FLlu3bqMiQj01FNPiTFR\nrFgxUde/7ykpKU67bt26ovbxxx+LXKJECZETExNFfu+99xgTEWjUqFG+uwvoB0W5ZWZm+r62PmZ0\nEydOZExEoAkTJniOiUt9z2fMmOFZ0w8V07Vp04bxELk8x8SOHTt8L3z//fc9a/ohkrrHH3+8UMcE\nd8YBAAAAQ5iMAwAAAIaEfJmKvgzl7NmzIvu9hay/NV2kSBHffOHChcD9RPjMnz9f5DZt2ohcoUIF\nkWfNmuW0hwwZImp33323yH/7299E1scIIlO7du1E1t8+TEtLE9m9ZK1Vq1aiNmzYMJHj4+NF3rRp\nU+B+AgBQ2JipAAAAAIYwGQcAAAAMYTIOAAAAGBLyNeMnTpwQOTo6WuTk5GSRf/jhB6cdGxsramXL\nlhX5yJEjIl9qayNEhooVK4qsbz948803i1ypUiWnPWLECFF74403RP7kk09EnjJlisiX2sIKZrRo\n0UJk/VmTzZs3i1y+fHmnrW9VuHz5cpGjoqJE1teYIzK9/PLLvvU//vGPnrXz58/7XquPCVwemjdv\n7llr3Lix77XPPPOMZ+1SY23atGn+HYMxTz75pGft1Vdf9b32rrvu8qwtXbo0aJcC4c44AAAAYAiT\ncQAAAMAQJuMAAACAISFfM75gwQKRc3JyRNaPL69du7bT1tcO6+sA9WPUL3X0KSLDkiVLRD558qTI\nJUuWFHnAgAFOW9+DfMWKFSK//vrrIo8ZMyZwPxE+e/fuFblRo0Yi688ZuI9C37hxo6iNHz9e5Cuv\nvFLk/v37i6z/nAEAIJy4Mw4AAAAYwmQcAAAAMITJOAAAAGBIyNeMFytWTOTVq1eLrK8pv+6665x2\n7969RS0hIUFkff150aJFA/cT4dOzZ0+R16xZI/KgQYNEdu8f//3334va2bNnRe7Vq5fI69atC9xP\nhE/9+vVFLleunMgXLlwQ+bPPPnPalmWJ2h/+8AeR9Z9B+nhDZLrzzjt961OnTvWsxcfH+16rP0eA\ny4PffuBffPGF77W/+93vPGv//Oc/A/cJZr333nuetbi4ON9re/To4VnTz7YINe6MAwAAAIYwGQcA\nAAAMCfkylcqVK4t87Ngx38/Pzs72/Fz9GOvjx4+LHB0dHaSLCLOvv/5aZPf3XCml9u/fL/KpU6c8\nP1ff2lBf2uT31iQiR5kyZUTWl57oS9BKlCjhtMuWLStqR48eFfmqq67yfS0AAEzizjgAAABgCJNx\nAAAAwBAm4wAAAIAhIV8zHhUVJXKtWrV86+415idOnBC1RYsWiexeN/pTr4XI1Lx5c5H179vHH38s\nsns9cenSpX0/97HHHhP5yJEjgfuJ8Hn88cdF1p8P6devn8juI+z1LagyMjJE3r17t8ixsbFBu4kw\natKkiW/97rvv9qwVKeJ/n0kfb7g86M+SuG3ZssX32gEDBnjW9O10dZ9++ql/x2DMnj17PGszZszw\nvbZTp06etZkzZwbuUxDcGQcAAAAMYTIOAAAAGMJkHAAAADAk5GvGq1WrJnLDhg1FLl++vMju9cH6\nPuMHDhwQ2bZtkfUjs/Wj0REZ9KPOixcvLvLAgQNFnjdvnue148ePF7lZs2Yinz59OnA/ET76GNDX\n+uv7ybv3Ctf3Fdd/pujPnrBmHAAQSbgzDgAAABjCZBwAAAAwhMk4AAAAYIilr7sGAAAAEB7cGQcA\nAAAMYTIOAAAAGMJkHAAAADCEyTgAAABgCJNxAAAAwJCQn8A5a9YssV3L2rVrRX3Pnj0iHzp0yGlf\nvHhR1IYMGSJybm6uyPopfiNGjLAK2F2EwZNPPinGxHXXXSfq+kmrRYoU8ay9//77IusnvmZkZIi8\ndOlSxkQEevvtt8WYaNOmjah/8803Ih8/ftxpr169WtS6dOki8o4dO0S+/vrrRe7fvz9jIgKlpaX5\nbvW1aNEiz9qqVat8X/uBBx7wrVerVo0xEYF69uzpOSZq167te+2oUaM8a/o8RHfzzTczHiLUHXfc\n4TkmGjRo4Hut32nM+lxCd9NNNxXqmODOOAAAAGAIk3EAAADAkJAvU1m8eLHISUlJIm/cuFHk0qVL\nO+2mTZuKWnR0tMiVKlUSOTs7O3A/ET6ffvqpyI899pjITz/9tMiJiYlOe+vWraJ29OhRka+66iqR\nExISAvcT4XPs2DGRy5QpI7L+fZ0yZYrTLlGihKidPXtW5PXr14us/9zo379/wToLAEAh4s44AAAA\nYAiTcQAAAMAQJuMAAACAISFfM758+XKRjxw5IrK+vrNr165Ou3nz5qKmr/XUtzLcvn170G4ijFq1\naiXy7NmzRda3FNqyZYvTrlevnqgNHjxYZH3MuK9F5OrQoYPI+hhYuXKlyDExMU67ZcuWonb+/HmR\n9S3Pzpw5E7ifCJ8VK1b41jt16uRZe/75532vffjhh33r77zzjm8dZuhb2brp29jq9L873L777rvA\nfYJZS5cu9az5bV2olFKTJ0/2rNm2786qhY474wAAAIAhTMYBAAAAQ5iMAwAAAIaEfM24vvd3VFSU\nyFWrVhXZvb+wvtYzPj5eZH1v4uTk5MD9RPiMGDFCZH0f6A0bNojsXvelr+Xcv3+/yPq+43369BHZ\n/UwCIkf9+vVF1p8l0dd7up8XqVGjhqjpa0dbt24tclxcXOB+AgBQ2LgzDgAAABjCZBwAAAAwhMk4\nAAAAYEjI14w3aNBA5CpVqois7x1+6623Ou3SpUuLmr429LPPPhM5KSkpcD8RPu+++65vvXHjxiK7\n1wfre0pfcYUcwvo+9iVLlgzSRYTZRx99JPK8efNEPn36tMjunxtt27YVNf1ZksqVK4tcvXp1kfW9\n6REZ9OdBdPp+8m5r1qzxvXbjxo2+dfYZj0wVK1b0rP3f//2f77Xly5f3rEVHRwfuE8y69957PWvX\nXXed77Xjx4/3rA0bNsz32mnTpvl3rIC4Mw4AAAAYwmQcAAAAMCTky1SOHz8usr5sRV+S4N7asFy5\ncqKmH4/81ltviXyptzURGfQtK9u3by/yq6++KnLPnj2ddosWLURt4cKFIutvHa1evTpwPxE+hw4d\nElnf3lKv9+rVy2nn5OSImr6kRV++pn9+3759C9ZZAAAKEXfGAQAAAEOYjAMAAACGMBkHAAAADAn5\nmvF9+/aJrK8P1qWkpDjtlStXitrUqVNF3r59u8j6WmREJv248nPnzom8d+9ekdPT0532qVOnRO3h\nhx8WuUgR+e/LtWvXBu4nwueWW24RuUmTJiLr25hmZmY6bX1bRPf2qD/12voWqYhMl9p+8O677/as\n2bbte21iYmKgPsGso0ePetb0+YLOPbfQxcfHB+4TzCpbtqxn7VLPEerPHboNHDgwaJcC4c44AAAA\nYAiTcQAAAMAQJuMAAACAISFfM+5e76uUUiVKlBBZXwOWlZXltPW1oPpeww0bNhS5R48egfuJ8MnN\nzRVZXw+clpYm8vDhwz0/96uvvhJ51qxZIod73ReCuXjxosiWZYl8xRXyR5X7uYI777xT1DZv3ixy\nQkKCyLGxsSL7HZMNAECocWccAAAAMITJOAAAAGAIk3EAAADAEOtS+7ECAAAACA3ujAMAAACGMBkH\nAAAADGEyDgAAABjCZBwAAAAwJOSH/gwdOlQ8IaofzNOuXTuRq1Wr5rR3794takWLFhU5MzNTZPeB\nQUop1bt3b3lyCCLC8uXLxZjYtWuXqHfs2FHkBQsWOO26deuKWlxcnMiffPKJyG+88YbIGRkZjIkI\nNG7cODEmypQpI+r6QVHuQ3/2798var179xb57NmzIj/xxBMi27bNmIhAaWlpvrsL3H///YFfe+XK\nlb713bt3MyYi0Ny5cz3HRHR0tO+1UVFRnrUuXbr4XsvPiMg1adIkzzFx7733+l5bsWJFz9rQoUN9\nr504cWKhjgnujAMAAACGMBkHAAAADAn5MpX69euLXKtWLZFr164tcrFixZx2q1atRO3ChQsi79mz\nR2S/t6EQOVq2bCmyPkaOHj0q8uHDh512WlqaqKWmpoo8b948kfWlTYhM69evF7lRo0YiV6lSRWT3\n933AgAGipi99e+aZZ0QuXrx44H4CAFDYuDMOAAAAGMJkHAAAADCEyTgAAABgSMjXjOvbDa1YsULk\nVatWidy4cWOnXbVqVVGLiYkR2b2+XCmlzp8/H7ifCJ+JEyeKbFlyh6D+/fuL3L59e6d98eJFUVu7\ndq3IZcuWFblSpUqB+4nwSUhIELl8+fIi16xZU+ScnBynnZ6eLmpLliwROTY2VuRfsiUewmfnzp2+\n9Z49e3rWEhMTfa8tXbp0oD7BLH0O4NahQwffa/XnidySkpIC9wlmTZ8+3bOmb2Or8/sZo881Qo07\n4wAAAIAhTMYBAAAAQ5iMAwAAAIaEfM349u3bRf76669F1o9Cb9q0qdOuXr26qLVu3VpkfY/plJQU\nkfVj1REZtmzZIrK+9/zw4cNFfuGFF5y2/j1t2LChyPoe0l999VXgfiJ8rr32WpF37Nghsr4ufM6c\nOU5b/znhfu5Eqf8+8lg/vwAAAJO4Mw4AAAAYwmQcAAAAMITJOAAAAGBIyNeMJycn+9b1vcIPHz7s\ntIsWLSpqZ86cETkzM1PkqKioIF1EmOnf8woVKog8dOhQkd977z2nrT9zoH/Pv//+e5H1fe0Rmfr0\n6SOyvkb81KlTIrv3ou/SpYuoHT9+XGR9D/Ps7OzA/UT4DBo0yLf+l7/8xbN2qX3EJ02aFKhPMCs3\nN9ezVqJECd9r/Z4h0583w+XDfeaETp9D6gYPHuxZ++ijjwL3KQjujAMAAACGMBkHAAAADAn5MhV9\nSUJcXJzI11xzjchVq1Z12jVq1BA1fYuyvXv3inypt6kQGfQlCWlpaSLv27fP8/PfffddUTt48KDI\nvXv3Fnnjxo2B+4nwsSxL5KysLJH1ZQfurQ83b94sahcuXBBZ3/pw//79QbsJAECh4844AAAAYAiT\ncQAAAMAQJuMAAACAISFfM65vSaavAy9SRP57wH1UdUxMjKhlZGSIXL58eZH1I7QRmfQ1vAsXLhQ5\nPj5e5AYNGjjt++67T9Q+/PBDkefNmydyt27dAvcT4bN48WKR9ec/oqOjPbO+feoHH3wgsv7cSosW\nLQL3E+HTo0cP3/rw4cM9ay+//LLvtZfa+rBmzZq+dZjRrl07z9qltqJLSkryrA0YMCBwn2BWw4YN\nPWv6s0c6vzEzderUwH0KgjvjAAAAgCFMxgEAAABDmIwDAAAAhoR8zfiWLVtE1teCNmrUSGT3uu9K\nlSqJWmpqqsjLli0T2e+oXESOyZMni/zDDz+IrH/f3Wt+9+zZI2rff/+9yC1bthTZb50gIse5c+dE\nrlatmshDhw4V2f2cgf6siH7EccmSJUUuXrx44H4CAFDYuDMOAAAAGMJkHAAAADCEyTgAAABgiGXb\ntuk+AAAAAL9J3BkHAAAADGEyDgAAABjCZBwAAAAwhMk4AAAAYAiTcQAAAMCQkJ/A+eyzz4rtWipW\nrCjqsbGxnvnUqVOidvjwYZF3794t8sWLF0WeMmWKVcDuIgxee+01MSY2btwo6vppix9//LHTvvXW\nW0VNv1Y/yTErK0vk++67jzERgTp27CjGxMqVK0W9SpUqIrdt29ZplylTRtQyMzNFrlevnsjTp08X\n+ejRo4yJCDR37lzfrb769u3rWbvULmFPPfWUb/3RRx9lTESgcePGeX5j3X9P/JQDBw541qKjo32v\nzcnJYTxEqPnz53uOCf2Ed93WrVs9a/PmzfO9dvLkyYU6JrgzDgAAABjCZBwAAAAwJOTLVMqWLSty\nw4YNZQeukF1ISUlx2kWLFvV97bp164pcokSJIF1EmDVp0kTkXr16iawvZapRo4bT/uyzz0QtOTlZ\n5DZt2og8f/78wP1E+Ozfv1/kxMREkcuVKydy06ZNnbY+Xk6fPi2yvmRBH28AAJjEnXEAAADAECbj\nAAAAgCFMxgEAAABDQr5m3L3eVymloqKiRNbXirq3K9S3NtRfKyYmRmTWjF8err/+epFfffVVkd3P\nDSilVG5urtPesWOHqO3bt0/k9PR0kW+++ebA/UT46H+2ExISRM7IyBD5yJEjnjX9tapVqyZydnZ2\n4H4ifJYuXepbnzp1qmftUs8FnD17NkiXYNjjjz/uWatQoYLvtbNnz/as/e1vfwvaJRimzyfcZs6c\n6Xut39aHjz76aOA+BcGdcQAAAMAQJuMAAACAISFfpuJeYqCUUtu3bxd5xowZIru3omvXrp2o/f73\nvxc5Pj5e5BMnTgTuJ8JHPxHx+PHjIp85c0Zk9xjSt8a87bbbRP72229Ffuedd0QePHhwwTqLsNDf\namzcuLHIX331lcjuZSzNmzcXNf3nwrp160SOi4sL3E8AAAobd8YBAAAAQ5iMAwAAAIYwGQcAAAAM\nCfmacf1Ie3198M6dO0UuVqyY046NjRU1PetHZB86dChwPxE+7777rsjffPONyP369RP55Zdfdto9\ne/YUNX0t8WOPPSZy586dA/cT4aNvY+r+OaDUf68pd68Ld2+HqpRSx44dE7lBgwYiX7x4MXA/ET7P\nPfecb71ixYqeNX2LU92ltsFDZJo+fbpnbejQob7XPvzww561v//9777XXnfddb51mLN3717PWu3a\ntX2vXbx4sWftUlsbzpkzx79jBcSdcQAAAMAQJuMAAACAIUzGAQAAAENCvmZc31dcX9/TqlUrkW+8\n8UbPWvXq1UXW14YeOHAgcD8RPqmpqSLr67r0tf8tW7Z02mlpaaKmP4MwYsQIkfv06RO4nwgffR13\nVlaWyPrzIi1atHDa+nMpxYsX9/xcpZQ6evRo4H4CAFDYuDMOAAAAGMJkHAAAADCEyTgAAABgSMjX\njOv7vZYoUULk7t27i9yrVy+nXbduXVHT14jPnz9f5CJF+LfF5aBUqVIi16tXT+Rt27aJ3Lp1a6d9\n9uxZUStZsqTI+r7i1apVC9xPhE+zZs1EbtKkich+z4tcuHBB1F588UWRExISRNZ/Bl1qj2GYMXDg\nQN+638/7+vXr+1575MiRQH2CWSkpKZ61ypUr+17rnlvobrnllsB9glnjx4/3rD3yyCO+1951112e\ntZkzZwbuUxDMXgEAAABDmIwDAAAAhoR8mYp+rPXp06dFjomJETkuLs5pW5YlaklJSSJnZ2eLrC9r\nQWSqUaOGyPq2dps3bxb5mmuucdrr168XtQkTJoisL11as2aNyO5tEhE59CUHs2fPFtn9c0EppXJy\ncpy2vhWm/nNCX8bSoEGDwP0EAKCwcWccAAAAMITJOAAAAGAIk3EAAADAEMu2bdN9AAAAAH6TuDMO\nAAAAGMJkHAAAADCEyTgAAABgCJNxAAAAwBAm4wAAAIAhIT+Bc+rUqWK7lpUrV4p6dHS0yO7TFm+4\n4QZRO3z4sMjbtm0T+ezZsyIPGTJEHuGJiDBx4kQxJo4ePSrq+imasbGxTls/QfPUqVMiX3GFHNI7\nduwQedWqVYyJCGRZlhgTAwcOFPVJkyaJnJmZ6bTLly8vaunp6SK//fbbIs+ZM0fkDRs2MCYi0KRJ\nk3y3+hozZoxnTT/BVbdz507f+oMPPsiYiEArVqzwHBM9evTwvfbee+/1rD377LO+19q2zXiIUD17\n9vQcEw8++KDvtfpp326X+hnx6quvFuqY4M44AAAAYAiTcQAAAMCQkC9TOXjwoG/es2ePyAcOHHDa\nFy9eFLVu3br55uTk5MD9RPjcf//9Iu/atUvkoUOHirxp0yanXaSI/PdjTk6OyMuXLxe5T58+gfuJ\n8NGXpZQpU0ZkfYlaUlKS03aPD6WUKlmypMj6MpYOHToE7icAAIWNO+MAAACAIUzGAQAAAEOYjAMA\nAACGhHzNeGJiosjfffedyIcOHRLZsv6zW8wPP/wganv37hU5JiZGZP3zW7VqVbDOIiz0dd379u0T\nWf8+b9261Wnr63/15wr0Lc2KFSsm8qOPPlqwziIsbrzxRpHT0tJEjouLE7lmzZpO2z0+lFKqU6dO\nIufm5opctWrVoN1EGC1cuNC3rj9H4HapZ0WOHDkSqE8wq3jx4p61cePG+V574cIFz9p7770XuE8w\nq2/fvp61nj17+l5brVq1wu5OYNwZBwAAAAxhMg4AAAAYwmQcAAAAMCTka8Zr164t8pAhQ0SuUaOG\nyEWLFnXa+l7DW7ZsETk6Olrk+Pj4wP1E+Jw+fVrkFStWiKw/C+Be3/nll1+Kmj5+9D2m9WcWEJlu\nuOEGkdetWydyamqqyO61o82bNxc1fR/7evXqiTx48ODA/QQAoLBxZxwAAAAwhMk4AAAAYAiTcQAA\nAMCQkK8Zb9u2rcj6fsH9+vUTOSkpyWmfOnVK1Hbv3i1ydna2yLZtB+4nwufgwYMit27dWuSMjAyR\nW7Zs6bSrVKkiavoe5eXKlRO5VKlSgfuJ8Dlz5ozI+p9997MkSil14MABp125cmVRu+2220Ru2LCh\nyDxHcHnYsGGDb/1Pf/qTZ00/c0LHXvOXp5SUFM+a/tyJbuLEiZ41/ZwDXD705xLd9Pmnrn///p61\nS51VUNi4Mw4AAAAYwmQcAAAAMCTky1T0ZQL68eXp6ekiu7em02s6fTnDuXPngnQRYbZ27VqR9e0I\n9aPQs7KynHb37t1FTX/rUR8T+tImRCZ9idmhQ4dE1pepuLc5vfbaa0Vt48aNIt9xxx0iN23aVOTZ\ns2cXrLMAABQi7owDAAAAhjAZBwAAAAxhMg4AAAAYEvI14/qWZfqa3u+//17kEydOOG19G8TNmzeL\nnJubK3JsbGzgfiJ8WrRoIbK+HrhixYoid+jQwWnv2LFD1N566y2Rc3JyRI6KihL51ltvLVhnERbu\n4+2VUur6668Xefv27SJfuHDBaVuWJWojR44UuXTp0iLrW6IiMh0/fty3/rvf/c6zpm9xqnvkkUcC\n9Qlm7dy507N23333+V6rb6nrNm3aNN9rO3fu7N8xGOO3PeGTTz7pe63fz4nPP//c91p9S+Zfijvj\nAAAAgCFMxgEAAABDmIwDAAAAhoR8zbi+Tmvv3r0i79q1S3boiv90qUgR+W8F/Qjj06dPi8ya8cvD\nvffeK7K+9t+9HlgpeZy5vrb4UvtR62PkmWeeKVhnERapqaki169fX+RVq1aJXLduXaedlJQkavrP\nCf1n0NKlS0UeMWJEgfoKAEBh4s44AAAAYAiTcQAAAMAQJuMAAACAIZZt26b7AAAAAPwmcWccAAAA\nMITJOAAAAGAIk3EAAADAECbjAAAAgCFMxgEAAABDQn4C52effSa2a3GfsKmUUsWKFfO8Vq+lpKSI\nfPbsWZH1EzsHDRpk/fyeIlxefPFFMSb0HX1eeeUVke+8806nXa1aNVGLi4sTWX+tEydOiDx06FDG\nRARq1qyZ+MaNHTtW1MuXLy+y++TedevWidrMmTNFbteuncju0zuVUmrGjBmMiQg0duxY362+li9f\n7lnr0aOH72s/++yzl/rtGRMRaMyYMZ5jQv85oGvVqpVnrVOnTr7XDhgwgPEQoWbMmOE5JvQ5om7q\n1KmeNf3vDd2kSZMKdUxwZxwAAAAwhMk4AAAAYEjIl6kULVrUNx84cEBky/rPnf9atWqJmv5WdXp6\nusjJycmB+4nwefPNN0XWlxfddtttIkdFRTntOXPmiNqTTz4p8tChQ0Vu27atbx2RYfDgwSI3adJE\n5PPnz4uckJDgtPU/93fddZfIjRo1EvnMmTOB+wkAQGHjzjgAAABgCJNxAAAAwBAm4wAAAIAhIV8z\nHh8fL/LFixdFzsjIEPmHH35w2u51oUopVb9+fZHLli0rck5OTuB+Inyef/55kd9//32RK1WqJPKs\nWbOctr4+eMyYMSL36tVL5Nzc3MD9RPh06dJFZH1bqc8++0zkHTt2OO2GDRv6vpb+nEqpUqUC9xPh\nc+zYMd969+7dPWsTJ070vfb06dO+9Zdeesm3DjP0Z4TcbrjhBt9rY2NjPWtr1qzxvXbAgAH+HYMx\n+vNGbvrcQte8eXPPWvHixQP3KQjujAMAAACGMBkHAAAADAn5MpWKFSuKrL/1uGrVKpHT0tKcdtOm\nTUWtTZs2IusndFavXj1wPxE+W7duFbl3794ib9myReR69eo57SpVqoiaPgaOHj0q8muvvSay39uc\nMOe7774Tef369SIvXbpU5BYtWjjt/v37i5q+NE5f9rR79+6g3QQAoNBxZxwAAAAwhMk4AAAAYAiT\ncQAAAMCQkK8Zr1GjhshZWVki62vIly1b5rQ7dOggap06dRLZfUy6UkpFR0cH7ifCR99WbOHChSLr\n2xfOmDHDac+bN0/U9GcOSpQoIfKECRMC9xPho68Rr1Wrlsj68yPVqlVz2vv27RM1ffvU9PR0kT/+\n+GOR3evPETnGjh3rW58/f75nTd8WV6c/t4LLw9133+1Z27Ztm++1X3/9tWdt8eLFgfsEs9auXetZ\nu+qqq3yvHTRokGdtyZIlgfsUBHfGAQAAAEOYjAMAAACGMBkHAAAADAn5mnGdbdsiJyYmiuxe66ev\nL9ev1deGxsXFFUYXEWIPPfSQyPfcc4/I+t701157rdMeMmSIqHXu3Flkfc24vl4YkUnfG7xPnz4i\nHwb3v2oAACAASURBVDx4UOQjR444bX2Pcv3nhj5G9L3qAQAwiTvjAAAAgCFMxgEAAABDmIwDAAAA\nhoR8zbi+rnvXrl0i62t8mzdv7rT1vYZPnDghsr6GPDMzU+TGjRsXrLMIi/vvv19k/fvYu3dvkd17\nhd9+++2i9sEHH4j873//W+SWLVsG7SbCqHjx4iJXqlRJ5JycHJHdewq7148r9d/rz/U9p/WfOYhM\nixYt8q3feOONnrWYmBjfa+vVqxeoTzBrypQpnjW/PaOVUurRRx/1rD311FO+1+p/RyFyTJ061bOm\nP3+me/LJJz1r9913n++1a9as8e9YAXFnHAAAADCEyTgAAABgSMiXqWzfvl3kPXv2iKy/Hd2kSROn\nXaZMGVHTtzfLzc0VuXz58oH7ifDRlxvpSxBSU1NFnjx5stPWlxjoS16WLVsm8vXXXy9yu3btCtZZ\nhMXvf/97kVetWiWyfuSxO+vLUPRlTk2bNhV5wYIFgfsJAEBh4844AAAAYAiTcQAAAMAQJuMAAACA\nIRZb9gAAAABmcGccAAAAMITJOAAAAGAIk3EAAADAECbjAAAAgCFMxgEAAABDQn4C50MPPSS2aylV\nqpSolytXTuSyZcs6bf0Ezvbt24scExMj8v79+0WuW7euVbDeIhyGDx8uxkRmZqao66dszpo1y2kP\nGzZM1Dp06CDywoULRX7sscdETkxMZExEoM2bN4sx4T51VSmlrrzySpG3bt3qtCdNmiRqixcvFrlb\nt24ir169WuS2bdsyJiKT71Zf3bt396xd6qTd//3f//WtN23alDERgV5//XXPMZGUlOR77dNPP+1Z\nu/32232vfeuttxgPEWrUqFGeY6JevXq+17733nuetQsXLvheu3bt2kIdE9wZBwAAAAxhMg4AAAAY\nEvJlKhUrVhQ5PT1d5LNnz4rsXrJw+PBhUatcubLItWrVEjktLU3kunXrFqyzCItnn31W5NGjR4v8\nxBNPiPyHP/zBac+YMUPUpk2bJvKQIUNE7t+/v8jr168vWGcRFg8//LDIx44dE/nmm28WOT4+3mnr\nf8537Ngh8ubNm0W+5ZZbAvcTAIDCxp1xAAAAwBAm4wAAAIAhTMYBAAAAQ0K+ZrxmzZoiN2nSRGT3\nVoZKyTXjGzZsELWDBw+KfPr0aZHPnz8ftJsII317wo0bN4pcvXp1kR944AGn3aZNG1F75plnRNa3\nO7vqqqsC9xPho29BNW7cOJGvvvpqkTdt2uS0mzdvLmozZ84UWd8+NSUlReQqVaoUrLMIi5EjR/rW\nlyxZ4lm74447fK/Vn0fSNW3a1LcOM+666y7P2ttvv+17bXR0tGdt4MCBgfsEs+677z7PWlZWlu+1\nY8aM8axt2bIlcJ+C4M44AAAAYAiTcQAAAMAQJuMAAACAISFfM56YmChyVFSU7MAVsgu7d+922qmp\nqaJWoUIF39du2LBh4H4ifL744guRX3nlFZG//PJLkevXr++0q1WrJmr6/tT6vvbXXHNN4H7CnClT\npoh85513ihwTE+O09X3D3XuQK/XfP3P0NeMAAJjEnXEAAADAECbjAAAAgCFMxgEAAABDQr5m/MKF\nCyLr+46vWrVK5Llz53rW9L1j9TXj+n7DiEzuNeBKKdW/f3+R09LSRN66davTrly5sqjpe0h//PHH\nIleqVEnkjh07FqyzCAv9jICjR4+KrD8P4v6z3qNHD1HLzc0VuUyZMiJv3749cD8RPn/961996717\n9w782hMmTPCt9+rVK/BrI3T0s0XcSpUq5XvtkCFDPGs7d+70vfa6667z7xiMcT8/pLvU99WvXqdO\nncB9CoI74wAAAIAhTMYBAAAAQ0K+TEXfau7kyZMi60feu7e9y8jIELWlS5eKnJ6eLrJ+3G379u0L\n1FeExzvvvCPySy+9JLL+VqR7iYJ+TPqOHTtEzszMFFlfJoXINH78eJFzcnJE3rZtm8jr16932u6l\nbUop1blzZ5H1JTArVqwQecCAAQXrLAAAhYg74wAAAIAhTMYBAAAAQ5iMAwAAAIZYtm2H9Df46KOP\nxG9w7tw5UV+wYIHI2dnZTlvfkkxfE56VlSVyo0aNRL7//vutAnYXYbBs2TIxJqpXry7q7jGglFId\nOnRw2n/5y19E7c033xT59ttvF3nevHkir1q1ijERgT788EMxJtzbWSqlVEJCgsibN2922o0bNxa1\n/fv3i6w/e+J+LkUppQ4fPsyYiECTJ0/2/cupWLFinrVLbXu4a9cu33qPHj0YExFo+vTpnmNi+vTp\nvtfGx8d71vy2TFRKqblz5zIeItS0adM8x8S0adN8r125cqVnbdmyZb7Xdu7cuVDHBHfGAQAAAEOY\njAMAAACGMBkHAAAADAn5PuPffvutyPrazxYtWojs3pdcP+7+1KlTIut7lOuvjcikH2nv3jNaKaUW\nLVok8rBhw5y2vsZL30N63759Inft2jVoNxFGR48eFblo0aIiV6hQQeTWrVs7bf0Zg6pVq4qcm5sr\nsv4sCgAAJnFnHAAAADCEyTgAAABgCJNxAAAAwJCQ7zMOAAAA4KdxZxwAAAAwhMk4AAAAYAiTcQAA\nAMAQJuMAAACAIUzGAQAAAENCfgLnuHHjxHYt/fr1E3X9lM2HHnrIaf/73/8WtS+++ELk0qVLi7x/\n/36RBw4caBWstwiHyZMnizFRpUoVUc/IyPC8du7cuSJfddVVIn/00UciDx06VOTRo0czJiLQoEGD\nxJioUaOGqGdlZYlcsmRJz9eaOnWqyPp46tatm8iLFy9mTESgxx9/3HerL7+TVPW/Z3S1a9e+1G/P\nmIhAd955p+eY0E/z1o0aNcqzZln+327bthkPEerzzz/3HBMLFizwvVY/DdxNP+1b9+mnnxbqmODO\nOAAAAGAIk3EAAADAkJAvU6lZs6bIc+bMEXnr1q0ib9u2zWnrbzEsWbJE5BMnToj8M956RARo2LCh\nyJdakvD222877V27donakSNHRK5Xr57Iq1atEnn06NEF6yzCYsSIESK3atVK5O+++05k9/f9rbfe\nErUiReQ9hq5du4rcrFmzoN0EAKDQcWccAAAAMITJOAAAAGAIk3EAAADAEMu2fXeP+sWOHz8ufoP0\n9HRRHzhwoMht2rRx2o0aNRK1pKQkkYsWLSrykCFDRG7VqhXbEUWg2267TYyJs2fPinrz5s1FLlWq\nlNM+ePCg72uvXbtWZP05A7aoikzffvutGBOHDh0Sdf37WK5cOad97tw5UTt9+rTI+haoW7ZsEfmT\nTz5hTESgypUr+/7lpP/ccNOfJ9LpzzLpkpKSGBMRaPPmzZ5j4lLbE77yyiuetZiYGN9rJ06cyHiI\nXJ5jYv369b4X6s8iuc2aNcv32kWLFrG1IQAAAPBrwGQcAAAAMCTkWxuWL19eZH2pyeDBg0U+fvy4\n027ZsqXva3Xs2FHkBx98UOQPPvigYJ1FWPzpT38SWd+esHr16iLHxsY67czMTFFLTk4WWX972r3s\nCZHr2LFjIuvLkfRTNN0ncMbFxYla/fr1Rda3OuzSpUvgfgIAUNi4Mw4AAAAYwmQcAAAAMITJOAAA\nAGBIyLc2HDdunPgN9C2l9K3o3P2pU6eOqFWuXFnkffv2iaxvcfb888+zHVEEuuaaa8SYOHDggKjP\nmzdP5OLFizttfU34nj17RM7NzRV59OjRImdmZjImItD7778vxoT+Z1l/1mT79u1OW3+WRP85odfd\nzyAopdTQoUMZExFoz549vn851a5d27P26aef+r72+PHjfevr169nTESg7t27e46JCRMm+F6rP2/k\ndt11113qt2Y8RKicnBzPMXGp54MSEhI8a/o8RFfY2yRzZxwAAAAwhMk4AAAAYAiTcQAAAMCQkO8z\nru8prdPXeV199dVOe9u2baKmr+/p06ePyE8//XSQLiLMFi1aJPLcuf+/vfsOr6pK+z6+Q2ghlJBA\naCFAgEAgNIURLDQFKaKMiGUuLDjqiAUfBxSdR0dFBsRRR1FxlFF8pIMioEMRpDcpSiDSQpUEAgkQ\nCISEkOT9b7/7vnXvPNlvzlmHd76fv9bvut1hzZVFWLNzn7W+FblZs2YiO6+s/fnnn0Vtw4YNIkdF\nRYnctm1b3/NE8Oh+zm3btomsr7pu1KiRPc7MzBQ1fc64zpUqVfI9TwAAyhtvxgEAAABD2IwDAAAA\nhrAZBwAAAAwJeM/4ypUrRe7YsaPI33zzjcjPPfecPR49erSo6Z7xjz76SOTOnTv7nieCJy0tTWR9\nzvgvv/wiclJSkj3u06ePqHXv3l3knJwckbdu3ep7nggefa74HXfc4fnfnzp1yh7Xrl1b1Jz95JZl\nWbm5uSLrHvPSzqKFGV9//bVnffDgwa611NRUz2effPJJX3OCWU2aNHGtOe8e+C179+51rU2fPt3z\n2S+++MJ7YjDmxRdfdK2V9jPE6+dEXFyc7zn5wZtxAAAAwBA24wAAAIAhAW9T0W0pq1evFnnYsGEi\nDxo0yB6fOHFC1G644QaRdbtDcnKy32kiiPLy8kTW15N///33Ip88edIeP/bYY6J27tw5kZ1HY1qW\nZTVt2tTvNBFEd911l8j6SnvdauL8OXL8+HFR061K2dnZIuvWpREjRpRprgAAlCfejAMAAACGsBkH\nAAAADGEzDgAAABgSVlJSYnoOAAAAwH8k3owDAAAAhrAZBwAAAAxhMw4AAAAYwmYcAAAAMITNOAAA\nAGBIwG/g7Ny5sziuRd/IWbt2bZGXL19ujxcuXChqTz75pMhdu3YV+dSpUyJPmjQprIzTRRCEhYWJ\nNVGjRg1Rv/POO0Xu1auXPT59+rTn1165cqXIR44cETk1NZU1EYKSk5PFmhg8eLCo//vf/xZ5x44d\n9jg8PFzU2rVrJ/JDDz0kcmpqqshTpkxhTYSgxYsXex71lZ+f71pbtmyZ59du3LixZ/2ll15iTYSg\n999/33VNvPrqq57PnjlzxrXWs2dPz2dXrVrFeghR3377reua6NKli+ez99xzj2tN3xb/G8p1TfBm\nHAAAADCEzTgAAABgSMDbVHTrSM2aNUVet26dyBEREfa4SZMmonbttdeK3L9/f8+vhdD0/PPPi6y/\nz/Hx8SK3aNHCHr/zzjuidtddd4m8YsUKkXfv3u17ngge/Xd73LhxIteqVUvkUaNG2eOTJ0+KWlpa\nmsi6ZUH/+nHKlCllmisAAOWJN+MAAACAIWzGAQAAAEPYjAMAAACGBLxn/JZbbhE5MzNT5DvuuEPk\nK1eu2ONJkyaJWlxcnMi6F7RixYD/z0E50P3BJSXyZKIJEyaIXK9ePXus+833798v8oYNG0T+6KOP\nfM8TwTNkyBCRnUcXWpZltWrVSuTi4mJ7rI+v1M9WrVpV5N/97nd+p4kgys7O9qyfP3/eteY8DvW3\nbNy40decYNZ7773nWuvdu7fnszExMa61xMRE33OCWV7HmM6fP9/z2YEDB7rWBg0a5PnsN9984z2x\nMuLNOAAAAGAIm3EAAADAEDbjAAAAgCEBb7KeOnWqyPraWX0VurPHXPeML1myRORjx46J7NVPhtCx\nfPlykfX3sVmzZiI/+uij9rhhw4ai1rVrV5H37NkjMueMXx2+/fZbkQcPHiyyXjNNmza1x5cvXxa1\n++67T+QffvhBZK9rsQEACDbejAMAAACGsBkHAAAADGEzDgAAABgS8J7xe++9V+S0tDSRFy5cKLLz\nTOnmzZuLmu4/v3jxosgFBQW+54ngKSwsFHncuHEi63PHnWeF6x7wAQMGeOY1a9b4nieCR//d1WeF\nR0REiHz48GF7rM+W79atm8ibNm0SuVOnTr7nieCZO3euZ7169equtXPnznk+u3TpUs+6/rwSQsOs\nWbNcay+99JLns3o/4aQ/i4Srh94HOuk9o/bCCy+41pKSknzPyQ/ejAMAAACGsBkHAAAADAl4m4pu\nE+jXr5/ILVu2FDkzM9Me618l6muvnb+qtqxfH2mG0JSRkSHyk08+KfLLL78ssrM15fjx46JWuXJl\nkWfMmCFytWrVfM8TwRMfHy+yblPR32fnVejO1jbL+vXRmP379xf51ltv9T1PAADKG2/GAQAAAEPY\njAMAAACGsBkHAAAADAl4z7i+1trZ62lZltWmTRuRo6Ki7LG+AlsfYfPss8965jfeeKNsk0VQtGjR\nQmR9bN1nn30m8pAhQ+xx9+7dRe2rr74SuVevXiJzZNXVoaioSOQ9e/aIrD9bUqVKFXusPxdw9uxZ\nz6zXG0JTbm6uZ/2pp55yrenPJmljx471NSeYpY+2dVq2bJnnsz/88INrbcmSJb7nBLP0UclOpR1R\nmp6e7lobM2aM7zn5wZtxAAAAwBA24wAAAIAhbMYBAAAAQwLeM/7999+LrPu2XnzxRZGdfeH6fOCR\nI0eKrM+rvvfee33PE8HTqlUrkQ8dOiSy/j7OmzfPHuse8REjRoj81ltviVy/fn2Rb7rpprJNFkGh\ne8R1L5/+u+7sCdb3DejPpejPDWzYsEHkvn37lm2yAACUI96MAwAAAIawGQcAAAAMYTMOAAAAGBJW\nUlJieg4AAADAfyTejAMAAACGsBkHAAAADGEzDgAAABjCZhwAAAAwhM04AAAAYEjAb+Ds2LGjOK6l\nUqVKoh4VFSVyjRo17HGFCvL/K8TGxoqcn58vckJCgsgvvfRSWBmniyD46aefxJr4xz/+IeonTpwQ\nOS8vzx7fddddolatWjWRs7KyRK5Zs6bII0eOZE2EoAEDBog10aJFC1FPTU0V2flzo02bNqJWpUoV\nkQsKCkTOzc0V+b333mNNhKDZs2d7HvV19OhR19rcuXM9v/awYcM8688++yxrIgSNGTPGdU0sXrzY\n89nWrVu71vTeQZs4cSLrIUQ1a9bMdU2UdlrgmTNnXGuDBw/2fPaLL74o1zXBm3EAAADAEDbjAAAA\ngCEBb1PJyMgQOTs7W+Qbb7xR5Lp169rjO+64Q9QuXLggsv51dNWqVX3PE8ETFiZ/u3Ps2DGRk5KS\nRO7du7c9njFjhqhde+21IicmJorct29f3/NE8GzdulXk6tWri9yxY0eRW7VqZY8XLFggal27dhX5\nypUrItepU8f3PAEAKG+8GQcAAAAMYTMOAAAAGMJmHAAAADAk4D3jnTt3FrmoqMiz7sx33nmnqOkj\n7/TXwtVh/fr1Ius+77Vr14r8xz/+0R7rfvIDBw6IfOutt4qs19DKlSvLNlkExYQJE0Ru3769yL/7\n3e9E3rVrlz0+ePCg5387c+ZMkfVnTRCali5d6lk/f/68a+3HH3/0fHb58uW+5gSzvI6q08efahUr\num93Jk+e7HtOMCslJcW1Nn78eM9np02b5lrTx+sGGm/GAQAAAEPYjAMAAACGBLxN5eGHHxZZ33ik\nb1E7efKkPU5LSxM1fSOn87+1rF/f7hkXF1e2ySIotm/fLrJuPVmxYoXIzpuw9G2dTZo0EfmTTz4R\nWd/witA0Z84ckXX72qVLl0R2Hod5/fXXi9rly5dF1r+eLu2mPgAAgok34wAAAIAhbMYBAAAAQ9iM\nAwAAAIYEvGf8+PHjIuu+7sLCQpEzMzPt8ZEjR0StuLhYZN0LqvvREZqcV5n/Ft0/vGrVKnv81ltv\nidptt90m8osvvijyli1b/EwRQXb33XeLrI8x1Z8zcH7WZN68eaLWrFkzkfV6e+KJJ3zPE8EzZswY\nz3pMTIxrbcCAAZ7Ppqene9ajo6M96zBDf77IadKkSZ7PPv/886610vYOdevW9Z4YjKlZs6ZrbcOG\nDZ7P3nPPPa61WbNmeT7717/+1XtiZcSbcQAAAMAQNuMAAACAIWzGAQAAAEMC3jMeHh4ucl5ensj6\nKvSsrCx7XKNGDVGLjY0VWfejO59F6CooKBB5wYIFIj/99NMif/vtt/b4ySefFLWMjAyR9dXnO3bs\nEPnee+8t22QRFEuWLBFZnw9fvXp1kb/44gt7rM8V132l9evXF/nChQu+5wkAQHnjzTgAAABgCJtx\nAAAAwBA24wAAAIAhAe8Z1/2a2dnZIkdERIjcsmVLe5yfny9q1apVE1n3HleowP+3uBr06tVL5Fdf\nfVXkN998U+TU1FR7rPt933jjDZGXLl0qcp8+ffxOE0FUtWpVkRs1aiRyZGSkyJ9++qk9Pnv2rKjp\nnzGrV68WWf/3r7/+epnmiuDQP981/RkiJ+c59L/l/fff96ynpKR41mHG8OHDXWte54hb1q//XXHS\nPxNw9QgLC3OtjRgxwvNZ5x0mmnMvGgzsXgEAAABD2IwDAAAAhgS8TUX/+kdfcd+pUyeRnb+u1r9+\nOHTokMhVqlQRWbexIDT9/PPPIj/11FMiv/LKKyI7jycsKioSNX3dvb62uF69er7nieDRVw/rljP9\nK+bf//739jgnJ0fU0tLSRNY/c06cOOF3mgAAlDvejAMAAACGsBkHAAAADGEzDgAAABgSVlJSYnoO\nAAAAwH8k3owDAAAAhrAZBwAAAAxhMw4AAAAYwmYcAAAAMITNOAAAAGBIwG/gfOSRR8RxLVeuXBH1\n+Ph4kRs3bmyPL126JGoNGzYUWd/A2axZM5GTk5PlFZ4ICatWrRJrok6dOqKub9H897//bY8LCgpE\n7csvvxR53759Iuv/Pjs7mzURgqZPny7WxLBhw0R90aJFIrds2dIex8XFiZo+IWrr1q0if/fddyJP\nnDiRNRGCJk+e7HnU1xNPPOFamzJliufX1rcAa++++y5rIgQNHz7cdU28/PLLns9u3rzZtTZ9+nTP\nZxcvXsx6CFF//vOfXddEr169PJ9dvHixa+3OO+/0fLZPnz7luiZ4Mw4AAAAYwmYcAAAAMCTgbSqn\nTp0SWbeptG7dWuSkpCR7fOHCBVHTbSv6a6WmpoqcnJxctskiKLKyskTu2rWryBERESIPHTrUHk+Y\nMEHUhg8fLrJeMxUrBnyJoxx07txZ5OzsbJF165KzRW3Dhg2itnHjRpEPHTok8owZM0SeOHFi2SYL\nAEA54s04AAAAYAibcQAAAMAQNuMAAACAIWH6GLBy/wPCwsQfoI+aad++vci1atWyxzfddJOopaen\ni/z555+LfPHiRZG3bt3KcUQhaO/evWJNFBcXi/qZM2dEdh4/tH37dlHTx9QlJCSI3KRJE5FXrlzJ\nmghBs2bNEmtC/11u3ry5yCdPnrTHx44dEzV9bF1+fr7Ia9asEfnEiROsiRA0duxYz3+cfvrpJ9fa\ngQMHPL925cqVPevbt29nTYQgvZ9wmj17tuez+vNETp06dfJ89pprrmE9hKi5c+e6romMjAzPZ72O\nPixtb9ypUyeONgQAAAD+f8BmHAAAADCEzTgAAABgSMAPYW7atKnIsbGxIuvevQEDBtjjlJQUUdu7\nd6/I1apVE/ncuXN+p4kgWrduncitWrUSWff8Xn/99fZYnzd98803i6w/Z6DPnEZoKioqErl69eoi\n67PpnfcXfPXVV57P6rsMhgwZ4nueAACUN96MAwAAAIawGQcAAAAMYTMOAAAAGBLwnnFNn/vcs2dP\nkSMjI+2xPlO6sLBQ5NLOIkZo6t+/v8hRUVEi675wZ0/w+fPnRU1/jmDVqlUi165d2/c8ETy6Jzwi\nIkLknJwckW+//XZ7/MADD4ja5s2bRdY95HoNITRNnTrVs56cnOxa058l0d577z1fc4JZ//3f/+1a\n8zpH3LIsa9myZa61evXqeT57zTXXeE8Mxtx9992utbAw76PAZ82a5VrT/wZppZ1NX1a8GQcAAAAM\nYTMOAAAAGBLwNpX4+HiR9fWj/fr1E9l5ZFnjxo1FbcOGDSLrXx01aNDA9zwRPPr68n379omsW5mi\no6PtccWKcsk614tlyWMQLcuyVq5c6XueCJ62bduKrP+u6+/7li1b7LH+9XRqaqrILVu2FLlFixa+\n5wkAQHnjzTgAAABgCJtxAAAAwBA24wAAAIAhAe8Zv+GGG0Tu2rWryLrn9+TJk/ZY95E2atRI5Pbt\n24us+0oRmk6fPi1ycXGxyLpfOCYmxh6vWLFC1K5cuSLygQMHRNbHJCI0Va5cWeTc3FyR9edDnGtm\n48aNopaYmCiyPoIqPz/f9zwRPLVq1fKsp6enu9ZKO+audevWvuYEs8aNG+dae/rppz2fHTx4sGtN\nH6+Lq4fXUbVTpkzxfFbvF5yef/5533PygzfjAAAAgCFsxgEAAABD2IwDAAAAhgS8ybpSpUoir169\nWuSioiKRnVfcHzp0SNQSEhJE1v3A06dPF7lDhw5lmiuC48MPPxRZ92/qXtCzZ8/a44KCAlEbOHCg\n639rWZZ15swZ3/NE8Ojef/13ff/+/SI7rzmuUaOGqP38888i658x7dq18z1PAADKG2/GAQAAAEPY\njAMAAACGsBkHAAAADAkrKSkxPQcAAADgPxJvxgEAAABD2IwDAAAAhrAZBwAAAAxhMw4AAAAYwmYc\nAAAAMCTgN3DOnz9fHNcSHh4u6llZWSJXrVrV9b/Nz88XWd+uqG/7HDlyZJiFkBMRESHWxD/+8Q9R\n//7770WOjo62xzNmzBC1sWPHiqzrw4YNE/nZZ59lTYSg7777TqwJ/Xd9zJgxIh87dswe9+zZU9SG\nDh0qsr6VtbCwUOTnnnuONRGCpkyZ4nnU17lz51xro0eP9vza+nZnLSEhgTURgho3buy6JpKTkz2f\nHTlypGutf//+pf3RrIcQ9fDDD7uuiXvvvdfz2b59+7rW9L8j2rx588p1TfBmHAAAADCEzTgAAABg\nSMDbVLKzs0WOiooSubi4WGRnq4n+VbX+9bL+NaX+2ghNBw8eFHnEiBEiN2zYUOTdu3fb4+HDh4ta\n/fr1Rb7//vtFLigo8D1PBE9ERITIVapUEXnmzJkiO3+uXLlyRdScbU2WJdePZVlWRkaG73kCAFDe\neDMOAAAAGMJmHAAAADCEzTgAAABgSMB7xjt37ixy9erVRXYeUWZZltWgQQN7XFRUJGpHjhwRHSlD\nDAAAIABJREFUuaREnmije8oRmnbt2iVy7969Rdbf1w8++MAe6yPL9u/fL7L+3ECFCvz/zavBqVOn\nRE5NTRW5bt26Il+8eNEeHz58WNRat24tsj4CNScnx/c8ETw9evTwrM+ZM8e1tnDhQs9nBw0a5GtO\nMGv79u2utdzcXM9nv/jiC9favHnzPJ/97LPPvCcGY9LS0lxr+vND2t133+1a+/LLL33PyQ92KgAA\nAIAhbMYBAAAAQwLeppKQkCCy/pWxbi1xHnUYGRkpaklJSSJfvnxZ5PT0dN/zRPDs3LlT5Jtvvlnk\n6dOni+y8oVMfe6jXxG233SZyaTdwITQ89thjIuu/+zExMSKfP3/eHnfv3l3UWrVqJXKNGjVE/vbb\nb33PEwCA8sabcQAAAMAQNuMAAACAIWzGAQAAAEMC3jP+8ccfi7x69WqRt23bJrLzWLJOnTqJWlxc\nnMj6Cu1KlSr5nSaCqE+fPiLr4y2XL18usvOYu6lTp4raihUrRNafGxg5cqTveSJ4BgwYILKzJ9yy\nfv19PXTokD2+4YYbRC0xMVFk5zGIlmVZeXl5vueJ4Dlw4IBnPSwszLVW2pFmXsciWpZl3XfffZ51\nmKGPQHUq7bMgY8aMca2NGzfO95xgVrdu3Vxreg+p6c8wOk2YMMH3nPzgzTgAAABgCJtxAAAAwBA2\n4wAAAIAhAe8Z37Fjh8jLli0TWV99vnv3bnuszxHPysoSuWrVqiJXrlzZ9zwRPHPnzhXZeba8ZVnW\n2LFjRX7hhRfsce3atUXtoYceEvm//uu/RO7Xr5/faSKIrly5InJRUZHIjRs3Frl69er2WPf9zZgx\nw/PP0p81AQDAJN6MAwAAAIawGQcAAAAMYTMOAAAAGBLwnnF9BqQ+E7hr164ix8fH22PdN6rPGt6z\nZ4/InB98dVi7dq3IaWlpIv/zn/8U2Xme8AMPPCBqzvViWZa1d+9ekRcvXizyE088UbbJIijuuOMO\nkStWlD+aoqKiRK5Q4f++R9BnD4eHh4ucn58vMp8tuToMHDjQs67/7jvpfzu0zZs3e9Y5Zzw06Tsq\nnDIzMz2fHTp0qGvt66+/9nx2/Pjx3hODMUlJSa41r3PpLcuyjhw54lrTn1kMNN6MAwAAAIawGQcA\nAAAMCXibir7W+vrrrxdZH08YGxvr+rX0r5s7dOggcm5urp8pIsicx9JZlmU999xzIh8/flxkZzvS\n5MmTRS0lJUXk5s2bi7xgwQLf80Tw6OMtK1WqJHK1atVEdv4c0cejLlmyRORjx46JXLduXd/zBACg\nvPFmHAAAADCEzTgAAABgCJtxAAAAwJAw3W8JAAAAIDh4Mw4AAAAYwmYcAAAAMITNOAAAAGAIm3EA\nAADAEDbjAAAAgCEBv4Hzyy+/FMe1JCQkiHpkZKTIJ0+etMfR0dGiVqtWLZH17Z2/cbNeWFnmiuDo\n3LmzWBO9evUS9e+++07kxo0b2+OjR4+KWmpqqsivvvqqyIWFhSKPGzeONRGCduzYIdbEli1bRL1i\nRfmjKiIiwh4vWrRI1PLy8kTu0qWLyPp2zzFjxrAmQtDrr7/uedTXdddd51pr376959euX79+aX88\nayIETZ061XVNXLx40fNZr/USExPj+WxCQgLrIUTNnTvXdU1Mnz7d89mtW7e61kaPHu357KhRo8p1\nTfBmHAAAADCEzTgAAABgSMDbVIqLi0U+fvy4yI0aNRL5zJkz9rioqEjUcnNzRa5Xr57IBQUFIsfF\nxZVtsgiK++67T+QOHTqIrNtWnK1Mr7/+uqh98MEHIr/77rsiJycn+54ngmf9+vUi61alFi1aiLx7\n9257fPbsWVHTLQr6V9C6vQ0AAJN4Mw4AAAAYwmYcAAAAMITNOAAAAGBIwHvGdb9meHi4yM6jDC1L\n9n9Wq1bN89m9e/eKXLt2bZHpGQ9NCxYsEPnSpUsi6yOo5s2bZ48HDBgganPmzBH5qaeeEnnSpEm+\n54ng+fLLL0V2Hl1oWZaVlZUl8pUrV+zxkCFDRC0jI0Nkvd769Onje54InoMHD3rWa9So4Vq7fPmy\n57NNmjTxrLdr186zDjOioqJcaxcuXPB8dsKECa617du3ez6rj9RF6PD6u/7JJ594PtugQQPX2uzZ\nsz2fHTVqlPfEyog34wAAAIAhbMYBAAAAQ9iMAwAAAIYEvGc8Pj5e5PT0dJFLSuRNpufOnbPH+tzw\n7Oxszz9L95N169btfz1PBE+bNm1E3rVrl8gJCQkiDxo0yB7rs+b1GdPTpk0TuWnTpn6niSC6/vrr\nRdZ9gPpzBM4r7vX3eOzYsSK3bt1a5IYNG/qdJgAA5Y434wAAAIAhbMYBAAAAQ9iMAwAAAIYEvGfc\n2QNuWb/uD9Z94Dt37rTHVapUEbWaNWuKnJ+fLzI94lcHffb8xx9/LPKyZctE3rNnjz3WPeK6/1yf\nPfzNN9/4nifM0WtEnzt+4sQJe7x+/XpRi46OFrmoqEhk/XMFoel//ud/POthYWGuNX3WvOZ1vrBl\ncc54qHL+vde2bt3q+ew777zjWqtXr57vOcGs5s2bu9bWrVvn+WxOTo5rTe9dA4034wAAAIAhbMYB\nAAAAQwLepqKvNN64caPIa9euFTkzM9Me9+3b17VmWZbVtm1bkQ8dOuR7nggefS3xp59+KvLTTz8t\nsvOqdH1U5qVLl0R2tjlZlmXdcsstvueJ4NFtKC1atBB57ty5Ijt//ah/DmRlZYmsr8kuLCwUeciQ\nIWWbLAAA5Yg34wAAAIAhbMYBAAAAQ9iMAwAAAIYEvGf8l19+EXnTpk0i66OKIiMj7bE+fqpy5coi\n62MRW7Vq5XueCJ6OHTuK3LlzZ5FXrlwpsvP68iZNmrjWLMuyDhw4ILLuH0Zo0sdZnjp1SuSCggKR\n69SpY4/Dw8NFTR9lqH9upKWl+Z4ngmfgwIGedf1zxEn/XND0GsHV4fLly661V1991fPZZs2audZK\nSkr8TgmGzZs3z7Wm/x3Rhg4d6lor7TOI+vNr/694Mw4AAAAYwmYcAAAAMITNOAAAAGBIwHvGt2/f\nLnJsbKzIuu+vdu3arl8rKipKZGd/uWVZVrVq1fxMEUGme8R1H7i+0j41NdUenz17VtT091zXExMT\nfc8TwdOlSxeRn3jiCZFTUlJcs+4J1/mbb74ReceOHb7nCQBAeePNOAAAAGAIm3EAAADAEDbjAAAA\ngCFhnK8JAAAAmMGbcQAAAMAQNuMAAACAIWzGAQAAAEPYjAMAAACGsBkHAAAADAn4DZwvv/yyOK6l\nV69eov7555+LfPfdd9vjr776StRuvvlmkffv3y9yXl6eyG+99VZY2WaLYLjrrrvEmoiOjhZ1fQvr\n+fPn7fGMGTNErV69eiIfOHBA5BEjRog8efJk1kQIWrdunVgTly9fFnV9a2atWrXscUJCgqjpG14v\nXLggsr7Ns3///qyJEDRlyhTPo7727NnjWmvYsKHn19a3/mpDhw5lTYSg5s2bu66JiRMnej77448/\nutbGjx9f2h/NeghRXbt2dV0TP/zwg+ezzzzzjGvt3XffLe2PLtc1wZtxAAAAwBA24wAAAIAhAW9T\nKSwsFHnmzJki6181xsbG2uOpU6eK2vr160WeNm2ayM2aNfM9TwSP83tsWZZVqVIlkXX7kbP1RLcg\n5Obmiuxsc7Isy6pTp47veSJ4zp07J/KlS5dErlhR/qhytp5UrVpV1GrWrCnytm3bXJ8FAMA03owD\nAAAAhrAZBwAAAAxhMw4AAAAYEvCecd0T3rp1a5H79OkjsrOfc/v27aKWn58vco8ePUQuLi72PU8E\nT5s2bUQuKioSWfd5O9fI6tWrRU2vpxMnTnhmhKasrCyRGzdu7JkzMzPtcU5OjqiNGzdOZF3v2bOn\n32kiiA4ePOhZ158XcXrhhRc8n920aZOvOcEs/XkQp7ffftvzWX0MrlOXLl08n926dav3xGDMn//8\nZ9faK6+84vms1+eHbrrpJs9n161b5z2xMuLNOAAAAGAIm3EAAADAkIC3qTh/nWxZv24liYuLE3nB\nggX2eNSoUaK2bNkykZcsWSKy/lU2QtPJkydF/uMf/yjy7NmzRb5y5Yo91reslpTIy7f0La2NGjXy\nPU8ET4UK8r2APt7yl19+ETk7O9sed+/eXdR0e0OVKlVEPn36tO95AgBQ3ngzDgAAABjCZhwAAAAw\nhM04AAAAYEjAe8YHDx4s/0B1rXVUVJTIERER9ri0K7KXL18ucnp6uu95Inj69u0rsr6u/NSpUyIv\nXLjQHuujrRISEkTevHmzyM2bNxf597//fdkmi6Bo0KCByD/88IPIEydOFDk5Odke6/Vy/vx5kR9+\n+GGR+RzB1WHNmjWedf133alXr16ez3IM7tVpx44drrVrr73W89nRo0e71viZcPU6dOiQa03vN7Vu\n3bq51vTnmAKNN+MAAACAIWzGAQAAAEPYjAMAAACGBLxnXJ8prc+Jrl27tshjx461x/q6Y32e8Ndf\nfy1yWlqayH/961/LNlkEhT4bvG3btiLrNdKuXTt73LRpU1H76quvRHb2Ev9WRmjSfX86Oz9LYlmW\nlZ+fb4/1XQXx8fEi6zUQGRnpe54AAJQ33owDAAAAhrAZBwAAAAxhMw4AAAAYEvCe8Z9//lnku+66\nS+R9+/aJ7DwLVPeb33777SK/9tprIj/++OO+54ng2bt3r8jR0dEit2nTRuTw8HB7PGXKFFEbOnSo\nyHq9JSYm+p4ngkef6dq7d2+R9fnyGzdutMf6e6zPDNafPTl+/LjIrVq1KttkERSl3Ruh/y1xcn6m\n4Ldcd911vuYEs1avXu1a+/DDDz2fXbFihWutTp06ns8+9dRTnnWY43Ue+GOPPeb57C233OJa69Kl\ni+85+cGbcQAAAMAQNuMAAACAIQFvU9HHjk2bNk3k2NhYkZ2tKS+//LKofffddyIfOHBA5Bo1avie\nJ4JHtxycOXNG5MqVK4vsPPpwxIgRotakSRORS7sGG6Hp8uXLIjvbUCzLsvbs2SNyQkKCPT548KCo\n6baUBg0aiNyxY0ff8wQAoLzxZhwAAAAwhM04AAAAYAibcQAAAMCQMH01OQAAAIDg4M04AAAAYAib\ncQAAAMAQNuMAAACAIWzGAQAAAEPYjAMAAACGBPwGTsuyxHEt+fn5onjo0CHXB9PS0kSuVKmSyHXq\n1BG5WrVqIicnJ4f976eJYPnDH/4g1kRUVJSod+jQQeQTJ07Y44oV5ZI9fvy4yPqmxuTkZJHff/99\n1kQIWrNmjVgTjRs3FnXnjZuWZVkrV660x/v27RO1hQsXipySkiJy9erVRU5LS2NNhKBnnnnG86iv\n/fv3u9YKCws9v/ZDDz3kWR82bBhrIgSlpKS4rolHHnnE89ldu3a51vS+5DewHkLU0qVLfR8J2K9f\nP9fa+PHjPZ/9y1/+Uq5rgjfjAAAAgCFsxgEAAABDAt6msnTpUpHj4+NF3rZtm8gZGRn2uGrVqqJ2\n6tQpkRs0aCByq1atRNYtCggNN910k8jt2rUTOTo6WuRz587Z48zMTFHLzs4WWa+vnJwc3/NE8Dhb\nkSzLsipUkO8JDh8+LLLz+75161ZR0z83unXrVh5TBAAgIHgzDgAAABjCZhwAAAAwhM04AAAAYEjA\ne8bDw8NFXr9+vcirVq0S2XlUXWRkpKiV1lc6ZMgQkW+99dayTRZBoY8kO3nypMjNmzd3ret+4NjY\nWJFr1qwp8tdff+17ngiejRs3iqw/76GPQC0oKLDH+nMB+mjMtm3biqzXG0LTo48+6lk/cuSIay03\nN9fz2Z9//tnPlGDYkiVLXGv682ea19GH+t8V7X9x9CEMSUpKcq2dPn3a89kvvvjCtda7d2/fc/KD\nN+MAAACAIWzGAQAAAEPYjAMAAACGBLxn/OLFiyLr3tD09HSRd+zYYY8vXLggak2bNhW5Tp06Im/Z\nssXvNBFETz/9tMjnz58XWV9XHhMTY4+vXLkianXr1hV50qRJIuszzRGaiouLRXb+HLAsy0pLSxP5\n/vvvt8c9e/YUtb1794qsP6eSl5fnd5oAAJQ73owDAAAAhrAZBwAAAAxhMw4AAAAYEvCe8X379oms\nzwTeuXOnyDVq1LDHbdq0ETV9/nRUVJTIly9f9j1PBM+cOXNEzsrKErlKlSoip6am2uN77rlH1FJS\nUkR2nj9tWZZVr1493/NE8Oi/y5988onIeo04P0egzxXXP3P0etJ/FkKTPmte27Nnj2tt165dns/G\nxcX5mhPM2r59u2uttJ/1LVu2dK09/vjjvucEs/S/DU7ffPON57Njx451rR09etT3nPzgzTgAAABg\nCJtxAAAAwJCAt6k0atRI5ISEBJH1r4xbtGjxm2PLsqwGDRqInJmZKXJ0dLTveSJ45s+fL7Kz5cCy\nfn2kZfv27e3x22+/LWrNmjUTWV+b3rp1a9/zRPDo77luLalQQb43cB6FuGnTJlGLj48XuV27diK3\nbdvW9zwBAChvvBkHAAAADGEzDgAAABjCZhwAAAAwJOA947rXs3bt2iInJiaK3KRJE3us+32bNm0q\n8sGDB0XOzs72O00EUXh4uMj6CKE6deqIvHDhQns8YMAAUZsxY4bIen2NHDnS9zwRPA0bNhS5U6dO\nnnXnEalVq1YVtVOnTokcGRkp8sWLF33PE8ETFhbmWf/Tn/7kWrt06ZLns1euXPE1J5hVv35911pp\nRxt6rQnWw9XL6+f5c8895/nsM88841r7+9//7vnshAkTvCdWRrwZBwAAAAxhMw4AAAAYwmYcAAAA\nMCTgPePdu3cXuWvXriJv3LhRZOc55LrfXPd86Z5C3TuK0JSUlCSyPi/+/PnzrvV//etfnl9bnyGd\nl5fnZ4oIstTUVJH1deX6zoH8/Hx7fOLECVHT59YfO3bMM996661lmywAAOWIN+MAAACAIWzGAQAA\nAEPYjAMAAACGhJWUlJieAwAAAPAfiTfjAAAAgCFsxgEAAABD2IwDAAAAhrAZBwAAAAxhMw4AAAAY\nEvAbOEeMGCGOa6lfv76ot2rVSuTIyEjX/7ZmzZoi65saf/zxR5H/9Kc/ySs6ERJGjx4t1oS+MVF7\n6KGH7PHnn38uavfff7/IW7duFfnDDz8UecWKFayJEDR16lSxJmbMmCHqX375pcg7d+60xzk5OaI2\na9YskevWrSvyoEGDRO7Tpw9rIjR5HvWVnp7uWnv77bc9v3C7du086w8//DBrIgTNnDnTdU3ovYTm\n/HdE0/9OaN27d2c9hKhp06a5rolNmzZ5PvvRRx+51vRt8b/xtct1TfBmHAAAADCEzTgAAABgSMDb\nVC5cuCDymTNnRC4sLBS5Ro0a9jguLk7UGjRoIHJeXp7n10Zomjp1qsh9+/YVuXv37iLPnj3bHg8Y\nMEDUXn31VZEjIiJEfuSRR/xOE0FUq1Ytkfv06SPyvn37RM7OzrbHsbGxoqbXyKJFi0R2trj81p8F\nAEAw8WYcAAAAMITNOAAAAGAIm3EAAADAkID3jGdmZopcUiJPoVm7dq3IlSpVsseNGzcWtRtuuEHk\nqKgokatVq+Z7ngie1157TeRXXnlF5CFDhoicn59vj9PS0kTt2muvFTkjI0PkU6dO+Z4ngqdNmzYi\nHz16VORdu3aJ7Py50rp1a1G7ePGiyPqoQ/0zCaEpLMz75LD+/fu71jZv3uz57Pz5833NCWZt2bLF\ntab3B5o+BtfJ+W8Mri5enxWcPHmy57N79+51rZV25HJ54804AAAAYAibcQAAAMCQgLep6F81njt3\nTmTdRuD8dbT+dbJuW+ndu7fI3bp1E/mmm24q22QRFIcOHRJ5/PjxIutfHTmPu0xMTBQ1fSvrqlWr\nRL58+bLveSJ4fvnlF5F120rnzp1F/tvf/maPmzdvLmq5ubkid+rUSeSzZ8+K7HWTIwAAgcabcQAA\nAMAQNuMAAACAIWzGAQAAAEMC3jP++OOPi1xcXCyy7g929vgWFRWJWk5Ojsg7duwQuWHDhr7nieB5\n6aWXRK5fv77Io0ePFrlevXr22Nk/blm/PsJs69atIhcUFPieJ4JHH1O6fft2kQ8fPixyXFycPXYe\nh2pZv/45kZqaKnKPHj18zxPB8+CDD3rW9+zZ41rTx6NqLVq08DUnmPXee++51r7//nvPZ/WxuE4b\nNmzwPSeY9dNPP7nWvv76a89n//CHP7jWjh8/7ntOfvBmHAAAADCEzTgAAABgCJtxAAAAwJCA94zX\nqlVL5JKSEpF1n3dSUpI9dvaFWtav+3915prrq0P79u1Fjo+PF1mfPe9cQ+Hh4aKme8Tvuecezz8L\noUn3B48aNUpk3RceGRlpj7dt2yZq+uz5QYMGiex1jToAAMHGm3EAAADAEDbjAAAAgCFsxgEAAABD\nAt4zfuTIEZF1n/fFixdFjo2NtccxMTGipvtGz507J7LuFUVoysjIELlPnz4i63NDnX3gf/3rX0Xt\nzjvvFLlZs2YiN23a1O80EUS6Z/z+++8X+bHHHhP5kUcescf6733r1q1FfuCBB0TWPeYITc7v8W95\n4403XGv6DgrtzJkznnX9eSWEhhdeeMG1pu8w0SZOnOhaS05O9nxW31WA0BEdHe1amzx5suez1113\nnWutVatWvufkB2/GAQAAAEPYjAMAAACGBLxNRV9pr9tUIiIiRHa2FVSpUkXUsrKyRNZXaJ84ccLv\nNBFE+ihD3WbQoUMHkefMmWOPhw4dKmq6xWXz5s0i9+vXz/c8ETy6TWXTpk0id+nSReSwsDB7vHPn\nTlHT7WspKSkib9myxfc8AQAob7wZBwAAAAxhMw4AAAAYwmYcAAAAMCRMX08PAAAAIDh4Mw4AAAAY\nwmYcAAAAMITNOAAAAGAIm3EAAADAEDbjAAAAgCEBv4Fz586d4riWX375RdSbN28ucq1atexxRkaG\nqJ09e1bkSpUqiXzx4kWRb7vttjALIefRRx8VayI7O1vUL126JHJaWpo9jo2N9fza+jbPzMxMkVNS\nUlgTIWjevHliTRQWFor60aNHRb7xxhvt8eLFi0VN37ipb+qdNWuWyCUlJayJENS3b1/Po76WL1/u\nWhs+fLjn165bt65nfeLEiayJELRo0SLXNVHavw1t27Z1raWnp3s+m5SUxHoIUW+//bbrmtB7CS0m\nJsa11qBBA89nBw8eXK5rgjfjAAAAgCFsxgEAAABDAt6mUrVqVZETExNFLioqEvn48eP2WLehNGnS\nxPPPOnbsmJ8pIsj0mujZs6fIe/bsEbl27dr2uGJFuWR1m1NERITIpf06GqFBt6AdOnRI5BUrVojs\n/Nmgn23ZsqXIWVlZIvfq1cv3PAEAKG+8GQcAAAAMYTMOAAAAGMJmHAAAADAk4D3j+rghfUTZDz/8\nILLzaDrdD6yPJtJH4un+c4SmBx98UGR93KXu8543b5491v3BderUEdnZX25Zvz4iD6Fp7dq1IkdH\nR4us18SZM2fsse4R37Fjh8inTp0SOT4+3vc8ETxNmzb1rK9bt861pj9zoO3evdvPlGDYm2++6Vqb\nP3++57P62Fun++67z/PZmTNnek8MxujPkTk98MADns+OHTvWtfbpp596PltS4nnyapnxZhwAAAAw\nhM04AAAAYAibcQAAAMCQgPeMHz58WGTd5zdjxgyRnWeJjxgxwvNrV6tWTeTIyEg/U0SQ6evJN27c\nKHJxcbHI+fn59rh79+6iVrlyZZFzc3NF1p9RQGjq16+fyElJSSL/9NNPIi9cuNAe657BAwcOiHz6\n9GmR6RkHAIQS3owDAAAAhrAZBwAAAAxhMw4AAAAYEvCe8ZMnT4q8c+dOkbds2SJyWlqaPR40aJCo\nXbp0SeRatWqJrHuNEZr0Gvjss89ETklJEblVq1b2WJ8lf8stt4isz5TWPeQITVWqVBFZnw+vzxKv\nV6+ePdafG7j55ptF1j3jzz33nO95Ing++eQTz/pXX33lWtOfQ9GqVq3qa04w6/rrr3etZWRkeD6r\nP2Pm5HVWNULbM88841or7XOHXueQV69e3fec/ODNOAAAAGAIm3EAAADAkID/bka/6tdX0jZu3Fjk\nRo0a2eOwsDBRq1BB/n+H9PR0kfVV6AhN+/fvF7ldu3Yi6/akbdu22WN9tKH+dfO1114r8o8//uh7\nngie+vXri5yXlyfy2bNnRe7Zs6c9dh59aVm//nW1/pkzb948kR988MEyzRUAgPLEm3EAAADAEDbj\nAAAAgCFsxgEAAABDAt4zro8jjI2NFfnGG28UuUmTJvZYH1mWk5Mjsr5WPSIiwvc8ETyHDx8WWV9f\nrr+PrVu3tsf6aMPo6GiR9XGXCQkJvueJ4NF/12NiYkTWR6Q6v69vvvmmqDVt2tQzZ2Zm+pwlgmnN\nmjWe9cTERNda586dPZ+dPHmyrznBrLi4ONfaggULPJ+9ePGia23cuHG+5wSzunTp4lrTnz/TOnXq\n5Fr717/+5XtOfvBmHAAAADCEzTgAAABgCJtxAAAAwJCA94xHRkaK7OwJt6xf9/gWFBTYY90frM8V\nLy4uFpkrjq8O+mx5fRb4unXrRA4PD7fHAwcO9Pza+qz5AQMG+Jkigkz/3W3Tpo3ImzdvFnn27Nn2\nWH8G4ejRoyLrnxvXXXed73kCAFDeeDMOAAAAGMJmHAAAADCEzTgAAABgSFhJSYnpOQAAAAD/kXgz\nDgAAABjCZhwAAAAwhM04AAAAYAibcQAAAMAQNuMAAACAIQG/gXPkyJHiuBZ9W97q1atFvnDhgj2O\niooStcLCQpEHDRokcrNmzUQeP358WNlmi2CYMmWKWBM9evQQ9dzcXJGdN3TOmzdP1Pbt2yeyXiMn\nTpwQuaSkhDURgl544QWxJqpXry7q7777rsh///vf7fHu3btFLTExUeS+ffuKrG8BtiyLNRGCunXr\n5nnU18cff+xa27p1a2lf27Pepk0b1kQIeuutt1zXxOjRoz2f3bJli2vt5MmTns8OGjT0NcR4AAAF\nYElEQVSI9RCixo0b57omXn75Zc9nx44d61rr3r2757M9evQo1zXBm3EAAADAEDbjAAAAgCEBb1Mp\n7deFHTt2FDkvL88eZ2RkiFp+fr7I+ldLERERfqaIILvxxhtFPnPmjMjXXHONyM2bN7fHNWvWFLXT\np0+LPGfOHJGHDRvme54InjFjxoi8bds2kfX3uWXLlvZ43bp1otagQQORDxw4IHJBQYHIuq0FAIBg\n4s04AAAAYAibcQAAAMAQNuMAAACAIQHvGY+JiRE5Pj5e5Dp16ojsPM5Q94QvWrRI5JISeaJNhQr8\nf4urwaVLl0S+cuWKyJUrVxb58uXL9ri4uFjUqlSpIvIrr7wi8gcffOB7ngieDRs2iKw/H9KvXz+R\na9WqZY8TEhI8v/Z7770n8n333ScyPeOhSf+c0CpWdP/n6/bbb/d89plnnvGsz5w507MOM9q1a+da\nW7t2reezf/nLX1xrDz30kN8pwTCvn//Ofyd+i/58kZP+zGKgsXsFAAAADGEzDgAAABgS8DYVfUOi\nvlkvOTlZZGfbwblz50StcePGIutjE7OysnzPE8Gjb9js0KGDyPPnzxfZ2X60dOlSUTt27JjIZ8+e\nFTknJ8f3PBE81apVE1n/6vHJJ58U2Xncpf5v9+zZI3Lr1q1F7tSpk+95AgBQ3ngzDgAAABjCZhwA\nAAAwhM04AAAAYEjAe8arVq0qsj7KsF69eiI7jzbUV2DrI/B0T3lpx9ggNERERIg8duxYkfWacPYH\nR0dHi9rRo0dFTkpKEnnNmjW+54ngWbx4sciDBw8WWa+JI0eO2GP9ORR9/KX+rElpR+YhNAwYMMCz\n/s9//tO1NmnSJM9n8/LyfM0JZukjUJ3i4uJ8P/vAAw/4nhPMyszMdK1NmDDB89ldu3a51t555x3f\nc/KDN+MAAACAIWzGAQAAAEPYjAMAAACGBLxnvFmzZiIPHDhQ5F69eol88uRJe6yvOte9xm3atBFZ\nn0GN0PTJJ5+IHBMTI7K+htZ5Xfm9994rao0aNRI5PT3d9VmErh49eojcpUsXkTdt2iTyokWL7HGf\nPn1ErVu3biKXlJSIvGXLFpE5dxwAYBJvxgEAAABD2IwDAAAAhrAZBwAAAAwJeM/48OHDRU5ISBBZ\nn0PuPCtcnxdcqVIlkXU/OmfHXh0uX74s8rRp00S+5ZZbRM7OzrbHS5YsEbVVq1aJfOjQIZF79+7t\ne54IHv35j507d4o8atQokV977TV73L17d1HLyckR+W9/+5vI3EdwddCfBdC8zgEu7e+9/rmBq8NT\nTz3lWqtQwfvd4vr1611r+n4KXD2WLVvmq2ZZljVu3DjXWmmfNzt48KD3xMqIN+MAAACAIWzGAQAA\nAEMC3qZSu3ZtkfXxhMePHxe5qKjIHufn54uaPqJMX2utr0pHaNJtA+PHj/f8753XGN96662iNmjQ\nIJF1G4uuIzRVrCh/FKWlpYncrl07kevVq2ePH3vsMVFLSUkRWbelFBYWivzSSy+VbbIAAJQj3owD\nAAAAhrAZBwAAAAxhMw4AAAAYEqb7sAEAAAAEB2/GAQAAAEPYjAMAAACGsBkHAAAADGEzDgAAABjC\nZhwAAAAwhM04AAAAYAibcQAAAMAQNuMAAACAIWzGAQAAAEPYjAMAAACGsBkHAAAADGEzDgAAABjC\nZhwAAAAwhM04AAAAYAibcQAAAMAQNuMAAACAIWzGAQAAAEPYjAMAAACGsBkHAAAADGEzDgAAABjC\nZhwAAAAwhM04AAAAYAibcQAAAMAQNuMAAACAIWzGAQAAAEPYjAMAAACGsBkHAAAADGEzDgAAABjC\nZhwAAAAw5P8AeaXQ5lfRP4MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a9600b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculating accuracy for each class separately for the test set\n",
    "result_cnn, Wc1, Wc2, Wc3, Wc4, Wc5, Wc6 = sess.run([y, W_conv1, W_conv2, W_conv3, W_conv4, W_conv5, W_conv6], feed_dict = {xin: test_data,  keep_prob_input: 1.0})\n",
    "\n",
    "#plotting extracted features (W)\n",
    "\n",
    "filt13 = np.zeros([3,22, 8, 8]);\n",
    "filt46 = np.zeros([3,22, 4, 4]);\n",
    "\n",
    "for i in range(22):\n",
    "    filt13[0,i,:,:] = Wc1[:,:,0,i];\n",
    "    filt13[1,i,:,:] = Wc2[:,:,0,i];\n",
    "    filt13[2,i,:,:] = Wc3[:,:,0,i];\n",
    "    filt46[0,i,:,:] = Wc4[:,:,i,i];\n",
    "    filt46[1,i,:,:] = Wc5[:,:,i,i];\n",
    "    filt46[2,i,:,:] = Wc6[:,:,i,i];\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10.0, 10.0) # set default size of plots\n",
    "for i in range(0, 22):\n",
    "    for j in range(0,3): \n",
    "        plt.subplot( 22, 6, 6*i+j+1)\n",
    "        plt.imshow(filt13[j,i,:,:])\n",
    "        plt.axis('off')\n",
    "        if(i==0):\n",
    "            plt.title('layer'+str(j+1))\n",
    "        \n",
    "        plt.subplot( 22, 6, 6*i+j+4)\n",
    "        plt.imshow(filt46[j,i,:,:])\n",
    "        plt.axis('off')\n",
    "        if(i==0):\n",
    "            plt.title('layer'+str(j+4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c6ecef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1,12):\n",
    "    plt.subplot(2,6, i)\n",
    "    plt.imshow(filt13[1,1+i,:,:])\n",
    "    plt.axis('off')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feeding the  CNN with some data (camera/file)\n",
    "\n",
    "Finally to test if the model really works it's needed to feed some new row and unlabeled data into the neural network.\n",
    "To do so some images are taken from the internet or could be taken directly from the camera."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D1rUAJlT56HPR2DgDDQ0NGrhEmZoYuIQQ2wMTAEzq7E6IHQB5FQohhCgUGriE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\nx+6z9Ur/oCZkDsIKair+OLgGx7hexy7R2hYi42ltcvGa2EX4MXbLrVF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c7ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-f37709bb961a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mwhile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# 2, 5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mfaces\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarked_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_faces_from_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'camera'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;31m# if some face was found in the image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'found '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfaces\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/patryk/git/emotion_recognition/utils.py\u001b[0m in \u001b[0;36mget_faces_from_img\u001b[0;34m(img_path)\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'camera'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0mvideo_capture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVideoCapture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         \u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvideo_capture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from IPython.display import clear_output\n",
    "#faces, marked_img = ut.get_faces_from_img('diff_emotions.jpg');\n",
    "#faces, marked_img = ut.get_faces_from_img('big_bang.png');\n",
    "plt.rcParams['figure.figsize'] = (5.0,5.0) # set default size of plots\n",
    "\n",
    "while(1): # 2, 5\n",
    "    faces, marked_img = ut.get_faces_from_img('camera');\n",
    "    # if some face was found in the image\n",
    "    print('found ' + str(len(faces)))\n",
    "    if(len(faces)):      \n",
    "        #creating the blank test vector\n",
    "        data_orig = np.zeros([n_train, 48,48])\n",
    "\n",
    "        #putting face data into the vector (only first few)\n",
    "        for i in range(0, len(faces)):\n",
    "            data_orig[i,:,:] = ut.contrast_stretch(faces[i,:,:]);\n",
    "\n",
    "            #preparing image and putting it into the batch \n",
    "\n",
    "            n = data_orig.shape[0];\n",
    "            data = np.zeros([n,48**2])\n",
    "            for i in range(n):\n",
    "                xx = data_orig[i,:,:]\n",
    "                xx -= np.mean(xx)\n",
    "                xx /= np.linalg.norm(xx);\n",
    "                data[i,:] = xx.reshape(2304); #np.reshape(xx,[-1])\n",
    "\n",
    "        result = sess.run([y], feed_dict={xin: data, keep_prob_input: 1.0})\n",
    "        nr_faces = len(faces);\n",
    "        \n",
    "        for i in range(0, nr_faces):\n",
    "            emotion_nr = np.argmax(result[0][i]);\n",
    "            plt.subplot( nr_faces, 2, (2*i)+1)\n",
    "            plt.imshow(np.reshape(data[i,:], (48,48)))\n",
    "            plt.axis('off')\n",
    "            plt.title(str_emotions[emotion_nr])\n",
    "            ax = plt.subplot(nr_faces, 2, (2*i)+2)\n",
    "            ax.bar(np.arange(nc) , result[0][i])\n",
    "            ax.set_xticklabels(str_emotions, rotation=45, rotation_mode=\"anchor\")\n",
    "            ax.set_yticks([])\n",
    "        clear_output()\n",
    "        plt.show()\n",
    "        # comment the line below for single execution\n",
    "        #break;\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Conclusions and comments\n",
    "- The data contains a lot of noisy data, i.e. faces are rotated and of different size. \n",
    "- A lot of emotions in the dataset were labeled wrong. (e.g. happy images in sad images).\n",
    "- (we think) That is why we couldn't achieve very good accuracy.\n",
    "- The accuracy is very good for \"Happy\" and \"Surprised\" class. These images seems to be the most \"clean\" as data. \n",
    "- The computational power to train CNN is very high, therefore it was very time consuming to try different computational graphs.\n",
    "- The facial emotion recognition is has very complicated features that were hard to explore for our computational graphs.\n",
    "\n",
    "# Possible improvements in the future\n",
    "- For sure the CNN would perfom better if the faces were always the same size and aligned straight. \n",
    "- We could try another, deeper CNN architectures to extract more features.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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